Estuaries and Coasts

, Volume 41, Issue 1, pp 177–192 | Cite as

Large-Scale Differences in Community Structure and Ecosystem Services of Eelgrass (Zostera marina) Beds Across Three Regions in Eastern Canada

  • Mizuho Namba
  • Heike K. Lotze
  • Allison L. Schmidt


Eelgrass (Zostera marina) forms extensive beds in temperate coastal and estuarine environments worldwide and provides important ecosystem services, including habitat for a wide range of species as well as nutrient cycling and carbon storage. However, little is known about how eelgrass ecosystem structure and services differ naturally among regions. Using large-scale field surveys, we examined differences in eelgrass bed structure, carbon and nitrogen storage, community composition, and habitat services across three distinct regions in Eastern Canada. We focused on eelgrass beds with low anthropogenic impacts to compare natural differences. In addition, we analyzed the relationships of eelgrass bed structure with environmental conditions, and species composition with bed structure and environmental conditions, to elucidate potential drivers of observed differences. Our results indicate that regional differences in eelgrass bed structure were weakly correlated with water column properties, whereas differences in carbon and nitrogen storage were mainly driven by differences in eelgrass biomass. There were distinct regional differences in species composition and diversity, which were particularly linked to temperature, as well as eelgrass bed structure indicating differences in habitat provision. Our results highlight natural regional differences in ecosystem structure and services which could inform spatial management and conservation strategies for eelgrass beds.


Eelgrass beds Ecosystem services Species composition Nutrient storage Coastal ecosystem 


Seagrasses form extensive beds in shallow water (Short and Neckles 1999), which provide a variety of essential ecosystem services (Barbier et al. 2010), such as trapping sediment, carbon storage and sequestration, nutrient cycling, habitat formation, and primary and secondary production (Costanza et al. 1997; de Groot et al. 2002). The creation of three-dimensional habitat, spawning and nursery grounds for various mobile organisms including commercially important fish and invertebrates, such as cod and lobsters, as well as substrata for the attachment of sessile epiphytic species (de Groot et al. 2002; Duffy 2006; Seitz et al. 2014), is one of the most important and widely recognized services of seagrass beds around the world (Nordlund et al. 2017). In addition, seagrass beds serve as significant “blue carbon” and nutrient stores (Fourqurean et al. 2012) due to their ability to accumulate organic carbon and other nutrients as biomass and within sediments (Macreadie et al. 2014). Seagrass ecosystem services vary across geographical regions and genera (Nordlund et al. 2017) as well as through spatial and temporal changes in extent and growth locally (e.g., Schückel et al. 2013; Saunders et al. 2015) and regionally (e.g., Thom et al. 2003; McDonald et al. 2016). However, little work has been done on examining how intra-specific, seasonal, or geographic variation could impact the provision of ecosystem services by particular seagrass species (Nordlund et al. 2017).

Despite their ecologically significant role, seagrass beds around the world are among the most threatened marine ecosystems (Duarte 2002; Waycott et al. 2009). Anthropogenic disturbances, especially coastal eutrophication, have resulted in the global decline of seagrass bed ecosystems (Orth et al. 2006; Waycott et al. 2009). In addition, climate change is a growing threat as rising sea levels and increasing ocean temperature may cause future seagrass losses (Duarte 2002; Saunders et al. 2013; Ondiviela et al. 2014). In recent years, the degradation of eelgrass (Zostera marina) beds in Eastern Canada has been documented and is linked to coastal eutrophication, the introduction of invasive species, and the aquaculture of commercially important bivalves (Garbary and Munro 2004; Malyshev and Quijón 2011; Schmidt et al. 2012, 2017; Skinner et al. 2013). However, because of its essential role as a habitat builder, wide distribution, and high abundance, eelgrass in eastern Canada is now classified as an Ecologically Significant Species (Fisheries and Oceans Canada 2009a), which is a first step in recognizing its ecological importance in the coastal ocean.

Although worldwide efforts to protect and restore seagrass ecosystems are increasing (Lotze et al. 2011) and some recoveries have been observed (e.g., in Tampa Bay, FL, see Greening and Janicki 2006), the loss of seagrass beds has resulted in a global decline in important ecosystem services such as the provision of nursery habitat and coastal water filtration (Worm et al. 2006). The consequences of the loss and possible recovery of eelgrass beds in Eastern Canada are difficult to measure because of the lack of baseline data (Garbary and Munro 2004) and potential differences in eelgrass ecosystem structure and services across regions. Previous studies in Eastern Canada have mainly been small-scale surveys focused on comparing the species compositions in eelgrass beds and adjacent bare sediment seafloor (Joseph et al. 2006), effects of invasive species such as the green crab Carcinus maenas on eelgrass beds (Malyshev and Quijón 2011; Garbary et al. 2014), habitat mapping (O’Neill et al. 2011), and acoustic surveys to examine landscape-level bed dynamics (Barrell and Grant 2013) at a single estuary or bay scale. In addition, larger-scale research comparing eelgrass and rockweed beds in Nova Scotia (Schmidt et al. 2011), analyzing the effects of bivalve aquaculture on eelgrass beds in New Brunswick (Skinner et al. 2013) and the effects of eutrophication across estuaries in New Brunswick and Prince Edward Island (Schmidt et al. 2012, 2017), has been done in the past. So far, however, no study has aimed at looking at the status of eelgrass beds with low anthropogenic impacts in Eastern Canada.

Although the boundaries of the three Eastern Canadian Provinces are historically and politically divided, the coastal ecosystems differ regionally (i) because of the different environmental conditions between the open Atlantic coast of Nova Scotia, the mainland coast of New Brunswick, and the island coast of Prince Edward Island (Galbraith et al. 2008; Petrie et al. 2008; Richaud et al. 2016), and (ii) in terms of provincially different strategies for the management of human activities affecting and conservation of coastal ecosystems (Fisheries and Oceans Canada 2009b) including eelgrass beds. For example, watersheds in Prince Edward Island are affected by nitrate loading from potato farms (Grizard 2013), whereas in New Brunswick, a part of nitrogen runoff is from peat mining (McIver et al. 2015), and the response of the eelgrass communities to these disparate sources is different (Schmidt et al. 2017). Understanding the differences in eelgrass ecosystems in bays and estuaries with low levels of nutrient loading would provide a baseline for regional- and provincial-scale management and conservation in the face of increasing human pressure in the coastal zone.

Classification of the patterns of biodiversity as well as the identification of ecosystem structure and services are essential to inform spatial planning of human uses and ecosystem based management strategies for the conservation of marine resources (Spalding et al. 2007; Halpern et al. 2010; Arkema et al. 2015). Our study aims to address the need for local baseline data as well as the knowledge gap around intra-specific and geographic variation in the provision of habitat and carbon and nitrogen storage services by examining regional differences in eelgrass habitat and community structure across sites with low anthropogenic impact in three regions in Eastern Canada. Specifically, we evaluate differences in (1) environmental variables, (2) eelgrass canopy structure, (3) the amount of carbon and nitrogen stored in eelgrass standing stock, and (4) the composition and diversity of the associated species assemblage. Finally, we also examine relationships among canopy structure, measured environmental variables, and species composition to explain patterns in eelgrass ecosystem structure and services among regions.

Materials and Methods

Study Sites

In Eastern Canada, eelgrass (Z. marina) can be found in parts of Labrador and Newfoundland, the southern Gulf of St. Lawrence, and along the Atlantic coast of Nova Scotia (Fisheries and Oceans Canada 2009a). A large-scale field survey was done using 10 sites located in three regions: (1) the open Atlantic coast of Nova Scotia (NS), and in estuaries along the (2) mainland shore of New Brunswick (NB) and (3) northern shore of Prince Edward Island (PEI), Canada. Data were collected from July 27–August 20, 2007 (Table 1; Fig. 1; Schmidt et al. 2011, 2012), when most mobile species are abundant (Schmidt and Scheibling 2007; Cullain 2014), and eelgrass is actively growing in Atlantic Canada (Cullain 2014). Seagrass tissue is known to be a good integrated measure of ambient water column nutrient concentrations (Lee et al. 2004), and all 10 sites were previously identified as having low levels of nutrient loading based on the low nitrogen concentration in the eelgrass tissues for their region (Schmidt et al. 2011, 2012). The NB and PEI sites were located within estuaries with NB sites being more brackish (Table 1) because of the higher freshwater input associated with the mainland, while the NS sites were in coves along sheltered and exposed shoreline. The bottom type at all sites was soft sediment, with sites in NS ranging from mud to sand sometimes interspersed with boulders whereas in both NB and PEI the bottom was mud. Eelgrass was the dominant meadow forming macrophyte at all sites (Schmidt et al. 2011, 2012).
Table 1

Environmental conditions in July and August 2007 at the ten study sites in three Eastern Canadian Provinces




Depth (m)

Water temp. (°C)

Salinity (‰)

Nova Scotia

 Franks George


N 44.60, W 63.90




 False Passage


N 44.74, W 62.80




 Musquodoboit Harbour


N 44.71, W 63.08




 Taylor’s Head


N 44.82, W 62.58




 Average ± SE


4.0 ± 0.4

14.9 ± 0.2

29.1 ± 0.8

New Brunswick

 Kouchibouguac Bay


N 46.84, W 64.94




 Tabusintac Bay


N 47.37, W 64.94




 Baie St. Simon Sud


N 47.73, W 64.77




 Average ± SE


0.85 ± 0.04

24.07 ± 0.53

27.17 ± 0.12

Prince Edward Island

 Freeland River Estuary


N 46.66, W 63.91




 Stanley-Trout River Estuary


N 46.47, W 63.46




 Mill River Estuary


N 46.77, W 64.08




 Average ± SE


0.87 ± 0.04

23.63 ± 0.31

29.47 ± 0.74

Fig. 1

Location of the study sites in three Atlantic Canadian Provinces. New Brunswick sites include SS, TB, and KB; Prince Edward Island sites include MR, FL, and ST; Nova Scotia sites include FG, MH, FP, and TH. See Table 1 for site abbreviations and details. The map was created by statistical software R (version 3.1.2; R Core Team 2014)

Sampling Design

For each site, a 50 × 4-m transect was laid parallel to the shore within the eelgrass bed ≥10 m from the bare sediment-meadow interface. Water depth at high tide and water temperature and salinity 20 cm below the surface were measured for each site (Schmidt et al. 2011, 2012). The transect was used for the assessment of highly mobile and pelagic species which were mainly fish and large decapod crustaceans but also included some pelagic invertebrates such as jellyfish. Eleven 0.5 × 0.5-m quadrats were placed along the transect at 5-m intervals and used to census the eelgrass canopy structure, associated mobile benthic fauna, and percent cover of sessile benthic species (on the bottom; hereafter sessile species) and epiphytic species (on blades). We used underwater visual census (UVC) to obtain a sample of the abundance and composition of highly mobile and pelagic species that was comparable across study sites. As such, we followed the UVC guidelines outlined by Mapstone and Ayling (1998) and the same person swam along each transect at both night and daytime high tide to observe diurnal patterns in the species assemblage. In addition, the same observer surveyed the density of each mobile benthic fauna species as well as the percent cover of each sessile and epiphytic species to the nearest 2% within each quadrat during daytime high tide.

Three sediment cores (0.2 m depth, 0.2 m diameter) were placed at three points (0, 25, and 50 m) along each transect to take eelgrass aboveground and belowground tissue samples as well as assess infauna abundance and composition. The samples were brought back to the shore and sieved through a 500-μm sieve to isolate the eelgrass roots and rhizomes and infauna. All species were identified to the lowest taxon on site if possible or brought back to the laboratory for further examination with a dissecting microscope.

Water Column Properties

Three 1-L water samples were taken manually by using a pipe sampler (2 m long, 2.5 cm diameter) above the transect center at each site three times during one tidal cycle including both high and low tide. Each water sample was placed in an opaque thermos bottle, filtered within an hour of collection, and frozen until returned to the laboratory to assess the amount of phytoplankton by using the chlorophyll a (Chl-a) concentration (μg/L) as a proxy, phaeopigments (Phaeo, μg/L) as well as total particulate matter (TPM, mg/L), and the proportion of particulate organic matter (POM, mg/L) and inorganic matter (PIM, mg/L). Each of the water samples were filtered using a Pall filtration rig with three 200-mL polysulfone filter funnels (19.1 mm in diameter) attached to a Welch piston vacuum pump (model 2522). For Chl-a and phaeopigments, 0.7-μm Whatman GF/F filters (2.5 cm diameter) were used to filter two replicate subsamples, and the filters were then placed in cryovials and stored in liquid nitrogen. TPM, POM, and PIM were estimated from filtering another two sets of subsamples through pre-combusted (450 °C at 6 h) and weighed 0.7-μm Whatman GF/F filters (2.5 cm diameter). The volume of the water filtered for the TPM samples varied between 50 and 100 mL but was constant within each site. The filters were then stored in a petri dish placed on ice after being rinsed twice with 5 mL of 2% ammonium formate to remove salts.

To determine Chl-a concentration without the interference of Chl-b, the Welschmeyer technique was used (Welschmeyer 1994): the GF/F filters were first suspended in 90% acetone at −20 °C for 24 h to extract particles attached to the filters, and the extracted samples were then placed in a Turner Designs 10-005R fluorometer. Phaeopigment concentration was determined by first converting Chl-a in the samples to phaeopigment by addition of 5 μL of 10% HCl and measuring extinction of the samples before and after the acidification (Strickland and Parsons 1972). To estimate the amount of POM in the TPM samples, the dry weight (measured after drying at 60 °C for 24 h) of each filter was subtracted from the combusted (PIM) weight (measured after placing in muffle furnace at 450 °C for 6 h).

Canopy Structure and Nutrient Content

Shoot density and canopy height were measured in each quadrat to assess the canopy structure of the eelgrass bed. The lengths of four randomly selected eelgrass shoots in a 0.25 × 0.25-m inset of each quadrat were averaged to get a representative measure of canopy height. Additionally, eelgrass above (blades) and below (rhizomes and roots) ground tissue were collected using sediment cores. All the aboveground tissues within the outline of a core were cut and placed in a bag. Once at the surface, the entire core sample was sieved to collect the belowground tissue. After removing epiphytes from the blades, both the aboveground and belowground tissues were weighed (wet weight) separately for each sample and then weighed again (dry weight) after drying in an oven at 70 °C for 48 h. Each sample of aboveground and belowground tissue was ground separately, and a 50-mg (dry weight) subsample was obtained to measure the amount of tissue carbon (C) and nitrogen (N) by a PerkinElmer Carbon Hydrogen Nitrogen 2400 Analyzer. To calculate C and N storage (g/m2), the percent C and N was multiplied to the respective aboveground and belowground biomass (dry weight g/m2) for each sample.

Statistical Analysis

The statistical program PRIMER (version 6.1.1) with PERMANOVA (version 1.0.1, PRIMER-E, Plymouth; Clarke and Gorley 2006) was used for all analyses. A factorial design with a fixed Province factor (three levels; NS, NB, and PEI) was used for all permutational analysis of variance (PERMANOVA) tests, except for highly mobile and pelagic species composition and associated diversity indices. For those, a fully crossed two-factorial design with fixed Province and time of the day (two levels; day and night) factors was used. When a significant Province effect was detected (α ≤ 0.05), permutational post hoc pairwise t tests were done to identify difference between Provinces (Anderson et al. 2008).

We used a separate multivariate PERMANOVA on the Euclidian distance matrix of each of the normalized (1) Chl-a and Phaeo, (2) POM and PIM, (3) shoot density and canopy height, (4) aboveground and belowground biomass, (5) aboveground and belowground C and N tissue content (%), and (6) aboveground and belowground C and N storage data to examine differences among Provinces in water column properties and eelgrass structure.

To examine overall patterns in species richness (S) of each assemblage component (highly mobile and pelagic species, mobile benthic fauna, sessile species, epiphytic species, and infauna) across Provinces, univariate PERMANOVA on the Euclidian distance matrix was used. Univariate PERMANOVA on the zero-adjusted Bray-Curtis similarity matrix was used on the square root-transformed total abundance (N). A separate multivariate PERMANOVA on the zero-adjusted Bray-Curtis similarity matrix of the square root-transformed abundance data for each assemblage component was used to assess the effect of Province and time of day (highly mobile and pelagic species only) on the community (Clarke and Warwick 2001). Cluster analysis of the distance between the site centroids for each assemblage component was done to visually represent the regional patterns in the eelgrass communities (Clarke and Warwick 2001). Similarity percentage (SIMPER) analysis was used when a significant Province effect was detected to identify species that were consistently contributing to the differences between Provinces (dissimilarity (Diss)/standard deviation (SD) ≥0.5; Clarke and Warwick 2001). Univariate PERMANOVA was then done for each of the SIMPER species using the square root-transformed abundance data in a zero-adjusted Bray-Curtis similarity matrix followed by post hoc t tests when a significant Province effect (α ≤ 0.05) was detected.

To explore the relationship between regional environmental variables and eelgrass canopy structure and species composition, Bio-Env+Stepwise (BEST) analysis was used separately on the Bray-Curtis similarity matrix of the square root-transformed overall assemblage composition using species presence/absence and each of the individual assemblage components with the Euclidean distance matrix of uncorrelated normalized environmental and canopy variables (see below for details). Prior to BEST analysis, we used a draftsman plot to assess the correlation between the 19 environmental and canopy variables (depth, temperature, salinity, Chl-a, Phaeo, POM, PIM, SD, CH, % C above and below, % N above and below, aboveground and belowground biomass, aboveground and belowground C and N storage (g/m2)) and only included variables that had a Pearson correlation ≤0.7. Because depth, temperature, and salinity were only measured at the site level, all other data was site-averaged before the correlation analysis. Based on the draftsman plot, we used a total of eight environmental (temperature, salinity, Chl-a, POM, PIM, aboveground %C and belowground %N) and three canopy variables (SD, CH, and belowground carbon storage). BEST analysis was used on the Euclidian distance matrix of the site-averaged and normalized canopy variables as well as a matrix of the normalized environmental variables to examine if the regional differences in eelgrass canopy structure could be linked to environmental conditions. We also used the Bray-Curtis similarity matrix of each of the site-averaged community components and a Euclidean distance matrix of normalized environmental and canopy variables to explore to what extent the regional differences in the species assemblage can be linked to the environment or canopy structure. For these analyses, all the possible combinations of the variables were tested with 99 permutation tests, and only the best of 10 results are shown. BEST uses Spearman’s rank correlations (rho, indicated as ρ s ) between each of the environmental and species composition or eelgrass structure matrices to find the best possible combination of variables that explain the pattern in species composition or eelgrass structure across Provinces (Clarke and Ainsworth 1993).


Environmental Measurements

The average water depth in NS was about four times deeper and water temperature 8 °C lower than in NB and PEI (Table 1). In contrast, the difference in the temperature between NB and PEI was only 0.44 °C. The salinity was lowest in NB followed by NS, and highest in PEI.

Multivariate PERMANOVA showed a significant effect of Province on Chl-a and phaeopigments, and PIM and POM (Table 2, Fig. 2). Chl-a and phaeopigments were significantly higher in the Gulf of St. Lawrence coast of NB and PEI than along the Atlantic Coast of NS, but there was no difference in phaeopigments between NB and PEI. POM was significantly lower in NS than PEI, and PIM was lower in NS than NB, but there were no other differences between Provinces.
Table 2

Multivariate and univariate PERMANOVA results showing the effect of Province on water column properties, eelgrass bed structure, and storage services across three Eastern Canadian Provinces






























Shoot density







Canopy height



























C storage—above










N storage—above






Significant values (p ≤ 0.05) are in bold. POM and PIM are water column particulate organic and inorganic matter; above is eelgrass shoots and below is eelgrass roots and rhizomes; C and N are carbon and nitrogen, %C and %N indicates tissue content

DF degrees of freedom, RDF residual DF

Fig. 2

Average (± SE) phytoplankton (Chl-a), phaeopigment, and total particulate matter (TPM) concentrations for the three Atlantic Canadian Provinces, Nova Scotia (NS), New Brunswick (NB), and Prince Edward Island (PEI). TPM is further broken down into its particulate organic matter (POM) and inorganic matter (PIM) constituents. Letters correspond to groups from the post hoc pairwise tests

Eelgrass Bed Structure and Nutrient Storage

The structure of the eelgrass beds was assessed at each site using shoot density (SD), canopy height (CH), and aboveground and belowground biomass (Fig. 3). Multivariate PERMANOVA indicated that canopy structure (SD and CH) but not biomass (aboveground and belowground) were significantly different among the Provinces (Table 2). Univariate PERMANOVA further showed that SD and CH varied among Provinces. The canopy was significantly taller in NS than in NB and PEI, with no differences between NB and PEI. Shoot density was 1.5 times higher in NB than in NS, and twice as high in PEI than in NB.
Fig. 3

Average (± SE) canopy height, shoot density, and aboveground (shoots) and belowground (roots and rhizomes) biomass (dry weight) for the three Atlantic Canadian Provinces, Nova Scotia (NS), New Brunswick (NB), and Prince Edward Island (PEI). Letters indicate significant difference between Provinces from the post hoc pairwise tests (α ≤ 0.05)

The effect of Province on the aboveground and belowground tissue carbon (C) and nitrogen (N) content (%) and C and N storage was significant (Table 2; Fig. 4). Both aboveground and belowground tissue % C was significantly lower in NB than in NS and PEI, while there was no difference between NS and PEI. Aboveground % N was significantly lower in PEI than in NS, but no effect was observed between any other Provinces or on belowground % N. Univariate PERMANOVA only detected a significant Province effect on belowground C and N storage but not on aboveground C or N (Table 2). Belowground C storage in NB was lower than in NS and PEI whereas belowground N storage in NB was only lower than in PEI.
Fig. 4

Average (± SE) aboveground (shoots) and belowground (roots and rhizomes) tissue C and N content (% of dry tissue weight) and C and N storage for the three Atlantic Canadian Provinces, Nova Scotia (NS), New Brunswick (NB), and Prince Edward Island (PEI). Letters correspond to groups from the post hoc pairwise tests

Community Structure

Cluster analysis showed a clear separation of the associated community composition by Provinces (Fig. 5). This was supported by the highly significant effect of Province on the overall community and each assemblage component (Table 3) except for the highly mobile and pelagic fauna, for which there was no significant effect of time of day, Province, or their interaction (P ≥ 0.068). Therefore, cluster and SIMPER analyses were not performed on this component of the species assemblage. For the overall community and infauna, NS was significantly different from NB and PEI, where the latter two Provinces formed a tighter cluster which was more distantly related to the assemblage in NS. For the mobile benthic fauna, sessile species, and epiphytic species, the three Provinces were all significantly different (all P ≤ 0.002).
Fig. 5

Cluster dendograms showing the regional similarity for the overall community (presence/absence) and the distance between centroids for the mobile benthic fauna, infauna, sessile species, and epiphytic species. Symbols represent the 10 sites across the three Atlantic Canadian Provinces, Nova Scotia (NS), New Brunswick (NB), and Prince Edward Island (PEI)

Table 3

Multivariate PERMANOVA results of the effect of the fixed Province factor on the overall community, mobile benthic fauna, infauna, sessile species, and benthic species composition and univariate results for each species identified by SIMPER as consistently contributing (Diss/SD ≥0.5 to the difference between the three Eastern Canadian Provinces











Mobile benthic





Asterias forbesi




Bittium alternatum




Crangon septemspinosa




Idotea spp.




Lacuna vincta




Littorina littorea




Littorina saxatilis




Mysis stenolepsis




Nassarius obsoletus




Pagurus acadiensis









Asychis elongata













Codium fragile ssp. tomentosoides




Fucus vesiculosus




Sphaerotrichia divaricata









Amphilectus lobatus








Membranipora membranacea




Spirorbis spp.




DF degrees of freedom, RDF residual DF

Total community richness based on presence/absence of all fauna and flora indicated that NB had the lowest richness (mean ± SE 15.3 ± 2.2), followed by NS (18.8 ± 0.7), and the highest richness was in PEI (23.7 ± 0.4). There was a significant effect of time of day (pseudo-F 1,14 = 7.2, P = 0.019) on highly mobile and pelagic species richness, where the richness was almost three times higher at night than during daytime (P = 0.024; Fig. 6), but there was no effect of Province or their interaction (P ≥ 0.44). In contrast, the richness for mobile benthic fauna (pseudo-F 2,106 = 6.1, P = 0.002), infauna (pseudo-F 2,27 = 4.8, P = 0.014), sessile species (pseudo-F 2,106 = 13.5, P = 0.001), and epiphytic species (pseudo-F 2,106 = 59.9, P = 0.001) differed significantly between Provinces. For both mobile benthic species and infauna, PEI has a significantly higher richness than NS and NB. In contrast, the richness of sessile species in NS was almost six times higher than in NB and 1.8 times higher than in PEI, and 3.4 times higher in PEI than in NB. For epiphytic species, richness was significantly lower in NB than in NS and PEI.
Fig. 6

Average (± SE) species richness and total abundance for highly mobile and pelagic species (day and night), mobile benthic fauna, infauna, sessile species, and epiphytic species found in three Atlantic Canadian Provinces, Nova Scotia (NS), New Brunswick (NB), and Prince Edward Island (PEI). The total abundance for highly mobile and pelagic species, mobile benthic fauna, and infauna was measured in number of individuals per square meter, while for benthic and epiphytic species it was measured in % cover. Letters correspond to groups from the post hoc pairwise tests

Total abundance of highly mobile and pelagic species was significantly higher during the night-time high tide (pseudo-F 1,14 = 8.7, P = 0.009; Fig. 6), but there was no effect of Province or their interaction (P ≥ 0.79). Whereas the total abundance of mobile benthic fauna did differ among Provinces (pseudo-F 2,106 = 17.8, P = 0.001) and was 4.4 times higher in PEI than in NS and 6 times higher in PEI than in NB (Fig. 6), mainly driven by the horn snail Bittium alternatum (Fig. 7). There was no significant Province effect on infauna total abundance (pseudo-F 2,27 = 1.3, P = 0.24), but there was for both sessile (pseudo-F 2,106 = 19.9, P = 0.001) and epiphytic species (pseudo-F 2,106 = 69.3, P = 0.001). NS had the highest sessile species abundance, mainly because of Fucus vesiculosus. PEI had three times greater abundance than NB mainly driven by the invasive green alga Codium fragile ssp. tomentosoides. Similarly, for epiphytic species, NS had the highest abundance, mainly due to the tube dwelling worm Spirorbis spp., and NB had a lower total abundance than PEI because of the near absence of epiphytic species.
Fig. 7

Average (± SE) abundance of SIMPER species that consistently contributed to the difference between Provinces among the three Atlantic Canadian Provinces, Nova Scotia (NS), New Brunswick (NB), and Prince Edward Island (PEI). Letters correspond to groups from the post hoc pairwise tests. Because of the large number of mobile benthic species that contribute to the differences, they span into the infauna sessile species columns and are distinguished by the dark gray bars

Species Driving the Difference Between Provinces

Ten species of mobile benthic fauna were identified by SIMPER as consistently contributing (Diss/SD ≥0.5) to the difference between Provinces, and all species differed significantly among the Provinces (Table 3). The gastropods Lacuna vincta and Littorina littorea, the hermit crab Pagurus acadiensis, and mysid shrimp Mysis stenolepsis (Fig.7) were all significantly more abundant in NS and either absent or rarely observed in NB and PEI. Bittium alternatum, the sea star Asterias forbesi, sand shrimp Crangon septemspinosa, and mud snail Nassarius obsoletus were all significantly more abundant in PEI than the other Provinces. Bittium alternatum was also more abundant in NB than in NS, and the gastropod Littorina saxatilis was more abundant in NB than in NS and PEI. The abundance of isopods Idotea spp. was significantly lower in NS than in NB and PEI, but no difference was observed between PEI and NB.

Seven species of infauna, three sessile, and four epiphytic species were identified by SIMPER as consistently contributing to the difference between Provinces (Table 3, Fig. 7). However, among the infauna, only the polychaete Asychis elongata and bivalves were significantly different between Provinces. Post hoc tests did not detect differences in the abundance of A. elongata among the Provinces because there were too few unique permutations, but clearly the abundance in NS is significantly greater than in NB and PEI. In contrast, the abundance of bivalves was significantly lower in NS than in NB and PEI, but no difference was observed between NB and PEI. The percentage cover of the three macroalgal species, the non-native green alga C. fragile ssp. tomentosoides, and the brown algae Sphaerotrichia divaricata, and F. vesiculosus, was relatively low, but were found almost exclusively in one of the three Provinces leading to the strong effect of Province. Codium fragile ssp. tomentosoides were only observed in PEI, and both S. divaricata and F. vesiculosus were significantly more abundant in NS than in NB and PEI. The abundance of two epiphytic species (the group of unidentified hydroids, Hydroidia, and the sponge Amphilectus lobatus) was highest in PEI. Hydroidia was also significantly more abundant in NS than in NB, but A. lobatus was not observed in either NS or NB. Spirorbis spp. and the bryozoan Membranipora membranacea were only seen in NS and therefore significantly different from other two Provinces.

Linking the Community Structure, Canopy Structure, and Environment Using BEST

The BEST analysis with the overall community against 10 environmental and canopy structure variables indicated that the overall community was best described by temperature and belowground carbon storage (Table 4) where temperature had the single highest correlation (ρ s  = 0.78). Highly mobile and pelagic species were best described by temperature, PIM, and belowground carbon storage whereas the mobile benthic fauna by temperature only. Infauna were best described by both temperature and belowground carbon storage; however, temperature was the single variable with the highest correlation (ρ s  = 0.64). Sessile species had the highest correlation with the combination of temperature, shoot density, and % C in aboveground tissue, and epiphytic species with temperature, Chl-a, PIM, canopy height, and % C in aboveground tissue with temperature again being the single variable with the highest correlation (ρ s  = 0.62).
Table 4

BEST analysis result of the best combination of the 10 environmental and eelgrass bed structure variables explaining the overall species composition based on presence/absence of data and each of the five community components across three Eastern Canadian Provinces



ρ s

Overall community

Temperature, belowground carbon storage


Highly mobile and pelagic

Temperature, PIM, belowground carbon storage


Mobile benthic fauna




Temperature, belowground carbon storage


Sessile species

Temperature, shoot density, above %C


Epiphytic species

Temperature, Chl-a, PIM, canopy height, and above %C


See Table 2 for abbreviations

ρ s corresponding rank correlation

Overall, eelgrass bed structure was not as well explained by the environmental variables. Shoot density was best described by the combination of Chl-a and temperature (ρ s  = 0.25), whereas canopy height was best described by temperature and salinity (ρ s  = 0.05), yet the correlation was weak in both cases. A slightly stronger correlation (ρ s  = 0.71) was observed when the combination of temperature and salinity was used to explain the pattern in aboveground biomass, whereas Chl-a was only weakly correlated (ρ s  = −0.21) with belowground biomass.


Our results suggest strong regional differences in the ecosystem structure and services of eelgrass beds across three Provinces in Eastern Canada. While the canopy structure was only weakly correlated with environmental differences among regions, the associated community composition was highly correlated with environmental conditions and partly with canopy structure. Although our study represents a snapshot in time since many of the environmental and biotic factors measured vary temporally across seasons and years, our measures of salinity and temperature are representative since they fall within the historical averages for the month of sampling within each region (Galbraith et al. 2008; Petrie et al. 2008; Richaud et al. 2016). Moreover, many of the components of the associated community were linked to the environmental conditions; therefore, it follows that our biotic measurements would also be representative of the community conditions during this time of year. As such, our results highlight distinct differences in storage and habitat services among regions with implications for regional-scale conservation and management.

Environmental Variables and Eelgrass Bed Structure

Previous studies have shown that the environment can influence the three-dimensional structure of eelgrass beds, where water depth, turbidity, salinity, and temperature are some of the important determining factors. The length of eelgrass blades (i.e., canopy height) and shoot density are influenced by water depth and turbidity since light is essential for photosynthesis (Larkum et al. 2006). Water depth and temperature were highly negatively correlated in our study, and therefore, depth was removed from our analysis. However, because canopy height had such a weak correlation with temperature and salinity, we re-ran the analysis with depth instead of temperature which resulted in a stronger correlation with depth only (ρ s  = 0.28). Overall, the canopy in this study was tallest and least dense in Nova Scotia (NS) where the water was clearest and deepest. Whereas in New Brunswick (NB) and Prince Edward Island (PEI), average water depth was less than 1 m, turbidity was higher, and the canopy was significantly denser and shorter. There was a weak correlation between shoot density and water column Chl-a concentration combined with temperature. The highest shoot density was observed in PEI, the region with the highest concentration of phytoplankton and warm waters. In addition, PEI, followed by NB, had the highest concentrations of POM, whose main constituents in coastal estuaries are living (Chl-a) and dead (phaeopigments) phytoplankton (Olesen 1996; Benson et al. 2013), and their presence decreases water clarity effectively attenuating light in the water column (Greening and Janicki 2006; Benson et al. 2013). This is in direct contrast to previous work linking increases in turbidity typically associated with nutrient loading with declines of eelgrass (Short et al. 1995; Olesen 1996; Kemp et al. 2004). Yet, we see that the eelgrass was getting enough light in NB and PEI since eelgrass in other estuaries in the region with higher total particulate matter and nitrogen content did show a reduced shoot density and taller canopy despite similarly shallow conditions (Schmidt et al. 2012). In Nova Scotia, the lower shoot density may be a strategy to avoid self-shading in deeper cooler waters where light would be more limiting as indicated by the taller canopy in this low turbidity region.

The Role of Eelgrass Beds in Nitrogen and Carbon Storage

Eelgrass beds are known to play an important role in nutrient storage in coastal ecosystems (Costanza et al. 1997; Duarte 2002). The high net primary production of seagrasses contributes to coastal carbon stocks through burial of organic carbon in sediments, export of detritus (Duarte 2002; Greiner et al. 2013), and storage within their tissues (Schmidt et al. 2011) where more carbon is found in the rhizomes and roots than in the leaves (Lee and Dunton 1996). We found that the carbon content of belowground tissues did vary among Provinces, with highest values in PEI, but these differences were not statistically significant. Greiner et al. (2013) recorded the average Z. marina tissue content from two study sites in Virginia, USA, to be between 36 and 52% carbon, which was comparatively higher than observed in this study. We found that the carbon content of belowground tissues did vary among Provinces, with highest values in PEI, but these differences were not statistically significant. However, the carbon contents of both aboveground and belowground tissues were significantly lower in NB than in NS and PEI whose carbon contents were similar. Therefore, the lower belowground carbon storage in NB and NS relative to PEI was due to the lower belowground biomass.

Nutrients such as nitrogen can be absorbed and retained by both seagrass leaves and roots, but leaves are known to play a major role in retaining and storing nitrogen (Hemminga et al. 1999). The nitrogen content in eelgrass leaves is also known to reflect ambient nutrient levels which are inversely correlated with leaf mass (Lee et al. 2004). Schmidt et al. (2012) found that eelgrass tissue nitrogen increased with eutrophication level but did not find any significant relationship between overall biomass and nitrogen content. In our study, all sites were classified as having low levels of eutrophication; therefore, the significantly higher nitrogen content in the aboveground tissue in NS was likely because the eelgrass beds are exposed to the upwelling events along the Atlantic coast which can be a significant source of nutrients (Hessing-Lewis and Hacker 2013). In addition, the higher concentration of Chl-a in both NB and PEI would also remove a lot of the nitrogen from the water column in estuaries with low nutrient loading and longer flushing times (Gregory et al. 1993; McIver et al. 2015) because the phytoplankton have higher turnover rates than eelgrass (Vadrucci et al. 2003; Huete-Ortega et al. 2014) and would take up the nitrogen (Buck et al. 2014) faster than the water is exchanged. The amount of nitrogen retained in the aboveground tissue did not differ among the Provinces because the differences (albeit not significant) in aboveground biomass offset the differences in tissue content leading to similar nitrogen storage in eelgrass blades among Provinces.

Associated Community Structure and Habitat Services

Cluster analysis of overall species composition and all the community components showed a clear distinction between the three Provinces likely linked to the different environmental conditions that therefore affect the habitat potential of the eelgrass beds. In all cases, NB and PEI were more similar to each other than to NS, indicating that the species present in eelgrass beds along the Atlantic coast of NS were different from the eelgrass beds along the southern Gulf of St. Lawrence coasts of NB and PEI. However, despite their similarity, the species composition along the mainland coast of NB and the island coast of PEI did have some differences. Our results showed that temperature was the single most influential and highly correlated variable with the overall community, highly mobile and pelagic fauna, mobile benthic fauna, infauna, sessile species, and epiphytic species. The difference in average sea surface temperature between NB and PEI was 0.44 °C, whereas the difference in temperature between NS and the two Gulf Provinces was more than 8 °C. Some fish species such as Atlantic cod Gadus morhua and invertebrates including the northern lacuna L. vincta and lacy crust bryozoan M. membranacea were only present in NS, although all have been documented in the Gulf of St. Lawrence (Mitchell 2000; Le Bris et al. 2013; Brickman 2014). For Atlantic cod, the water temperature in NB and PEI exceeds their temperature tolerance of 19 °C (Righton et al. 2010) which likely explains why they were only observed in the cooler waters of NS.

Diel movements of fish and decapod species in eelgrass beds led to higher species abundances and richness at night which suggests that many species were migrating into the eelgrass from neighboring habitats (Sogard and Able 1994; Nagelkerken et al. 2000) likely because they feed on prey that also forage at night (Sogard and Able 1994; Joseph et al. 2006). Despite the differences in time of day, there was no difference in the mobile and pelagic fauna assemblage between Provinces. This is not surprising because previous work done in NB estuaries found that the fish species observed in NB were 60 and 83% similar to those in the fish assemblages in Cape Cod, Massachusetts, and Damariscotta River, Maine, respectively (Joseph et al. 2006). Therefore, the close proximity of the Provinces would lead to correspondingly high similarity in the highly mobile and pelagic fauna assemblages despite differences in the physical environment.

Both species richness and abundance of the mobile benthic fauna were the highest in PEI and were not different between NS and NB. Five of the nine SIMPER species observed in this study (L. vincta, L. littorea, L. saxatilis, B. alternatum, and N. obsoletus) were gastropods that are common in eelgrass communities (Boström and Bonsdorff 1997). Previous work found that the overall abundance of molluscan species is positively correlated with seawater temperature (Urra et al. 2013), and our results show that L. vincta and L. littorea were more abundant in cooler water, meanwhile B. alternatum, N. obsoletus, and L. saxatilis were more abundant in warmer water. Temperature is known to affect the physiology of poikilotherms, and the costs associated with thermal metabolic stress can limit their distribution (McMahon and Russell-Hunter 1977; Sokolova and Pörtner 2001; Somero 2002). The average water temperature in NB and PEI is near the peak of L. vincta’s oxygen uptake rate, and because they are subtidal species, they do not exhibit metabolic adjustment at higher temperatures (McMahon and Russell-Hunter 1977). L. vincta can occur at very high densities on kelp (Laminaria spp.; Johnson and Mann 1986; Krumhansl and Scheibling 2011), one of their preferred foods (Johnson and Mann 1986; Chavanich and Harris 2002), but they are known to feed on other algal species (Chavanich and Harris 2002). Kelp, which require cold nutrient-rich waters, form extensive subtidal beds along the rocky Atlantic coast of NS (Filbee-Dexter et al. 2016), often within the same bay or along the same coastline as eelgrass beds (Schmidt, pers. observ.). Due to the proximity of the kelp beds, L. vincta’s high dispersal capabilities (Johnson and Mann 1986), higher abundance of benthic algae in our NS eelgrass beds, and metabolic effects explain their higher abundance in cooler waters of NS. Although L. saxatilis has a peak oxygen consumption similar to L. vincta, it does not show the drastic drop in consumption and is able to enter a reversible torpor at higher temperatures (McMahon and Russell-Hunter 1977) making it better suited to withstand the higher peak summer temperatures in NB and PEI estuaries.

The total abundance of infauna was not different among the three Provinces, yet species richness was higher in PEI and there was a significant difference between Provinces for the polychaete A. elongata as well as bivalves. Previous studies have shown that differences in shoot density (Webster et al. 1998), patch size (Mills and Berkenbusch 2009), and habitat complexity (Bologna and Heck 2000) may affect the spatial patterns in abundance and composition of infauna species. Interestingly, we found that temperature as suggested by Kuk-Dzul et al. (2012) was important in determining the spatial differences in the infauna community.

Sessile species composition was weakly correlated with the combination of temperature, shoot density, and aboveground tissue carbon content indicating that our sampling did not capture the most important factor. Sessile species, composed mostly of algae, were significantly richer and more abundant in NS followed by PEI. The close proximity of the eelgrass beds to fucoid beds (dominated by Ascophyllum nodosum) can help to explain the higher species richness and abundance in NS since the fucoid beds have higher sessile species diversity especially at the edge of the habitats (Schmidt et al. 2011), and these species may be transported to the adjacent eelgrass beds. The lower abundance and richness of sessile species in PEI and NB indicates that these sites lack nearby source populations likely because they are located within estuaries without rocky habitats to sustain algal beds in addition to low diversity of seaweeds in the region due to physical stresses such as ice scour (Scrosati and Heaven 2007).

Aboveground seagrass leaves provide substrata for various epiphytic species including algae and invertebrates (Borowitzka et al. 2006). Each of the four species that contributed to the provincial differences in species composition was either present or significantly more abundant in only one Province. The epiphyte species assemblage was best described by the combination of temperature, Chl-a, PIM, canopy height, and belowground carbon storage, where temperature alone was the most influential variable. Temperature is known to interact with other biotic and abiotic factors (e.g., nutrient availability, water motion) to control epiphyte abundance and diversity (Borowitzka et al. 2006). For example, M. membranacea has been found in cooler water temperatures (Saunders and Metaxas 2007), and the recruitment of the hard tube worm Spirorbis spp. is also more successful at lower temperature (Ushakova 2003). In addition, filter- and suspension-feeding epiphytes such as sponges and hydroids on seagrass leaves are known to play an important role in removing particles and filtering the water column (Lemmens et al. 1996; Borowitzka et al. 2006). Yet, few filter- or suspension-feeding epiphytes were observed in NB despite having intermediate Chl-a concentrations, possibly due to the high PIM interfering with food intake (Akoumianaki and Nicolaidou 2007). On the other hand, PEI had the highest Chl-a concentrations and supported an abundant and diverse epiphyte community likely because of the lower PIM and higher phytoplankton concentrations provide more edible particles to the suspension feeders (Lemmens et al. 1996).


This study demonstrates regional differences in environmental variables, canopy structure, carbon and nitrogen storage services, associated floral and faunal communities, and habitat services among eelgrass beds at low-impacted sites across three Eastern Canadian Provinces. Correlations among environmental variables, canopy structure, and species components suggest that multiple biotic and abiotic factors interact to form the observed regional patterns. Water temperature was the most influential variable in determining species composition. This suggests that the observed patterns are mainly driven by factors influencing the species’ physiological and biological requirements and the habitat potential of eelgrass in the different regions. Future management plans should consider both the large- and small-scale regional differences in eelgrass ecosystem structure and services in Eastern Canada in order to provide more effective and Province-specific conservation strategies for eelgrass beds and coastal ecosystems. Moreover, expansion of this study to other areas of the temperate North Atlantic would give us a broader picture of the mechanisms driving differences in eelgrass ecosystems among regions, providing further insight for conservation and management in a future facing ocean warming.



We thank M. Coll, J. Wysmyk, A. Battersby, K. Varsava, J. Lindley, and AquaPrime Mussel Ranch for field support, M. Kienast for CN analysis, and D. Ibarra for support with water samples. Our thanks go to Kouchibouguac National Park, Taylor Head Provincial Park, Spry Bay Campground, all those who hosted us or granted us access to our study sites in Nova Scotia, New Brunswick, and Prince Edward Island, and the anonymous reviewers who have improved this manuscript. This work was funded by a National Science and Engineering Research Council (NSERC) Discovery grant to HKL, a NSERC Post Graduate Scholarship—Doctoral and Killam Trust Predoctoral Scholarship to ALS, and the Sarah Lawson Research Scholarship and the Gary Hicks Memorial Award to MN.

Supplementary material

12237_2017_271_MOESM1_ESM.pdf (276 kb)
ESM 1 (PDF 275 kb)


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Copyright information

© Coastal and Estuarine Research Federation 2017

Authors and Affiliations

  1. 1.Department of BiologyDalhousie UniversityHalifaxCanada
  2. 2.Akkeshi Marine Station, Field Science Center for Northern BiosphereHokkaido UniversityAkkeshiJapan

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