The Community Structure, and Ecology, in and Around a Sabellaria Alveolata Biogenic Reef

The community structure, and ecology, in and around a

Sabellaria alveolata biogenic reef.

Abstract

Cei-Bach is a semi sheltered bay within the Cardigan Bay Special Area of Conservation

(SAC) in West Wales, and designated for its honeycomb worm (Sabellaria alveolata) biogenic reef habitat, which provides a biodiverse substratum on an otherwise scouring benthos. The study objectives were to make a rapid and effective assessment of the community structure and ecology with limited resources. GIS was used to measure the reef extent and environmental gradients for direct comparison with taxa response.

With some 52 species of macro-epifauna identified, there was much noise in the data and challenges in identifying the key players shaping the community. Indirect ordination techniques of Cluster Analysis and Principal Components Analysis (PCA) were used with

MVSP to resolve three clear community assemblages and their defining species. This enabled direct ordination with five environmental variables of shore position, stability, salinity, turbulence and submersion through Canonical Correspondence Analysis (CCA); whilst mitigating the characteristic “horseshoe” effect when resolving noisy data, or rare taxa, with Correspondence Analysis.

The results showed significant heterogeneity in the community structure and higher biodiversity within the reef extent. The reef was effectively “framed” by limiting factors of transition from an intertidal environment to the North; desiccation to the south, and east; excessive seston and salinity reducing inundation of freshwater to the west, where the honeycomb worm was competitively excluded by functioning guilds of Ulva sp. The study found that the assemblages were defined most strongly by shore position, substrate stability and salinity, and highlighted the challenges of effective environmental variable selection in direct ordination.

SID 0917866 Undergraduate Project Abstract TITLE

The community structure, and ecology, in and around a Sabellaria alveolata biogenic reef.

By 0917866, B.Sc. (Hons.) Marine Biology, Ecology and Conservation,

Anglia Ruskin University.

TABLE OF CONTENTS

Page

2. Introduction

5. Methods and results

9. Ordination methods flow chart

40. Discussion

46. Conclusions

47. Critique

48. Acknowledgements

48. References

57. Appendices

1. Species list

2. Raw data summary

3. CD ROM – Project data

INTRODUCTION

The objectives of this study are to gain an understanding of the defining communities within a distinct coastal ecosystem, and how biotic and abiotic influences shape these. The study focuses on the polychaete, Sabellaria alveolata (Linnaeus, 1767), at the intertidal area of Cei

Bach in Cardigan Bay, West Wales (Fig. 1). S. alveolata, the honeycomb worm, is an important ecosystem engineer, and UK Biodiversity Action Plan (BAP) species, as it creates littoral biogenic reef substrate which facilitates increased biodiversity (Maddock, 2008).

Cei – Bach is a semi-sheltered sandy bay with deposited terminal moraine, left by retreating glaciers some 11000 -18000 years ago (Crampton, 1965; CCW, 2013). The resultant boulder and cobble shore area, at the interface of the bay and River Llethi, provides a refuge of stability for settling epifauna and flora within an otherwise moving and scouring environment (Geograph, 2013; Little et al, 2009). This S. alveolata habitat is part of the

Special Area of Conservation (SAC) designation and towards the northern limit of the polychaetes’ geographic range (Fig. 1; Desroy et al, 2011; Moore, 2009).

Cardigan Bay SAC

Cei Bach

(Image: Moore, 2009) Image: NBN, 2012

Figure 1. The location of the study area at Cei Bach, within the Cardigan Bay Special Area of Conservation designation and, (right) UK distribution of S. alveolata (Cardigan Bay SAC,

2008; NBN, 2012).

Sabellariidae recruit sand grains through mucus secretion to create thick walled tubes, at right angles to the substratum, which aggregate and cement to each other like cells in a honeycomb (Fig. 2; Ruppert et al, 2004).

Figure 2. S. alveolata reef formation (left) and uncased worm (right; Image: Moore, 2009)

S. alveolata uses ciliated radioles to sort the sand grains into suitable sizes for tube building and suspension feeding (Hayward & Ryland, 1995). Unsuitable particles are rejected, and particle size, availability and distribution affect clearance and particulate retention efficiency

(Dubois et al, 2003 & 2005). Fresh water outfalls alter the hydro-sedimentary system which affects the duration of seston suspension, particle distribution, feeding and settlement patterns of S. alveolata and other dispersing taxa (Aller & Cochran, 1976; Aller et al, 1980;

Dubois et al, 2009; Pawlik, 1988).

My study examines the influence of autochthonous, endogenous and allochthonous environmental factors on the successful settlement distribution of S. alveolata, and taxa assemblage patterns within the terminal moraine (Little et al, 2009). The theory of the study animal, and other assemblage defining taxa, such as Mytilus, as a foundation species is considered, within the distinct community assemblages (Albrecht, 1998; Little et al, 2009). I further theorise that assemblage influencing factors will include, shore position and

3 desiccation, substrate stability, salinity, turbulence, submersion, particle deposition and size, competition and facilitation (Dubois et al, 2006; Pawlik, 1988). Anthropogenic pollution will be considered; specifically River Llethi eutrophication from agricultural run-off and pollution risk from the Llanina long sea treated sewage outfall emanating from the study area (Dodds,

2002 & 2006; EAW, 2012).

The hypotheses are that the distribution of S. alveolata will be heterogeneous within the sample frame; that distinct community assemblages will be defined by key taxa and associated definable areas (zones); I also hypothesise that there will be distinct taxa and assemblage response to environmental and endogenous factors.

As budget, equipment and human resources were limited, simple GIS methods were combined with traditional ecological census techniques, and a number of proxies adopted to measure environmental variables. Ecological and environmental data were collected over a six week period in May and June 2012. Basic descriptive statistics were calculated before analysis with indirect and direct ordination techniques to infer community structure and response of the key taxa and assemblages, to environmental factors. The key ordination outcomes were tested for statistical significance (Anderson, 2001). The methods and results are presented in a combined format and a flow diagram is presented on page 9, summarising the ordination process and methods, prior to significance testing (Table 2;

Ridley et al, 2010).

I discuss the distribution of the community assemblages within the boulder and cobble intertidal area, before focusing on the three assemblage defining taxa, S. alveolata, Mytilus edulis and Enteromorpha intestinalis. The ecology of these species and their response to the most correlated endogenous and exogenous environmental factors, interspecific competition, association and facilitation is then discussed.

METHODS AND RESULTS

A pre-site survey visit was made in March 2012 to consider experimental design and adaptations from methodologies in Joint Nature Conservation Committee (JNCC) guidance and the Countryside Council for Wales (CCW) commissioned intertidal surveys (Allen et al,

2002; Boyes & Allen, 2008; Davies, 2001a,b; Davies et al, 2001; JNCC, 2004; Moore, 2009).

Methods were designed to enable one surveyor, with a safety observer, to commence top- down surveys two hours before low tides. Lower shore surveying effort was enabled by synchronisation of lower shore sampling with Spring tides. Best practice was adopted from the JNCC handbook for monitoring of Annex 1 habitats, to ensure non-invasive and non- destructive methods, and no core samples were taken (Davies, 2001a; Davies et al, 2001;

JNCC, 2004 & 2012; UKMPA Centre, 2012).

Five pre-survey visits were made to ensure consistent identification of fauna and flora and the consistent assessment of live S. alveolata worm numbers, adopting the “lipped porch diagnosis” method from Boyes & Allen p. 21 (2008; Moore, 2009). The extent of the S. alveolata reef was assessed in accordance with Hendrick & Foster-Smith (2006) criteria for

“reefiness”, from which a clear ecotone could be identified as the reef boundary (Park,

2008). The reef boundary (Fig. 3; Zone SQ) was tracked with Garmin Map 62 handheld device, accurate to 10m with 95% confidence, with map datum WGS84 setting (Garmin,

2012; GPS Information, 2013). The zones neighbouring the reef in the boulder and cobble intertidal shore area, were tracked similarly; Zone NQ to the west and Zone MQ to the south- east (Fig. 3).

Tracks and waypoints were downloaded, with GPS Utility, into Quantum GIS and zipped to polygons for each reef zone (GPS Utility, 2012; QGIS, 2010). The sample frame was groundtruthed, with Cei-Bach landmarks, on OS Map TL38 downloaded from Digimap (2012;

Fig. 3; QGIS, 2010).

Moore (2009) made criticism of the original Boyes & Allen (2008) survey which used 2 x 2m quadrats and a very low sampling frequency. Random stratified sampling was also considered for lower, upper and middle shore zones but rejected in favour of random cluster sampling, as GPS logging of each random quadrat allows stratification of the data to be reconsidered after the survey (Bolam et al, 2011). By using gridded 0.5m x 0.5m quadrats with 25 equal cells, sampling frequency, accuracy and statistical power was increased

(Ausden & Drake, 2006; Kent, 2012; Moore, 2009).

Moore (2009), randomly located quadrat sites along transects by way of Microsoft Excel random numbers and “closed eyes” in placing the quadrats upon the stratum as no other cheap and simple method was available. Whilst GPS locations of each sample were logged by Moore (2009), it was not effective to copy the same locations as the extent and location of the reef is dynamic (Lancaster & Savage, 2008).

To reduce potential bias, Quantum was used to generate random samples in the zone polygons; 12 in Zone MQ, 24 in Zone SQ and 22 in Zone NQ, in accordance with the sampling effort guidance of Davies (2001a); based on the reef extent survey in May 2007

(Davies et al, 2001; Greenwood & Robinson, 2006; JNCC, 2004 & 2012; QGIS, 2010). The random generated waypoints were uploaded to the GPS unit and the “Find” function used to locate each random sample point, within the accuracy tolerance of the unit. To maximise precision, the quadrat was placed touching the surveyors toes immediately upon “0m” distance to sample appearing on the unit, and the actual sample waypoint captured

(Greenwood & Robinson, 2006).

A data card was designed to enable consistent collection and logical MVSP, Excel and

SPSS data input. The number of live S. alveolata worms, identification and presence/absence of epifauna and flora, and coverage (%) of S. alveolata, algae, Mytilus,

Cirrepedia and standing water (submersion), were recorded. Dominant substrate type was categorised as sand, granule, pebble, cobble or boulder as a measure of environmental

6 substrate stability (Wentworth scale, in Little, 2000). One sand sample was collected when available, at every other quadrat and stored in zipped labelled polythene bags for laboratory data analysis of sediment size and sorting (Wentworth, 1922).

GPS tracks were logged for; the centre of the River Llethi channel, as a proxy for salinity i.e. increasing distance from freshwater influence, and the centre of the high water jetsam strandline as a datum for shore position. The waypoint was logged at a substantial boulder marking the low water extent of the freshwater channel outflow as a proxy for turbulence influence (Fig. 3 (a, b, c); Aller & Cochran, 1976; Aller et al, 1980; Dubois et al, 2009). The

Quantum “ruler” function was used to measure euclidean distance from the tracks and waypoint to each actual sample waypoint (QGIS, 2010).

N 1 1 c SQ NQ 3

MQ 3 b Cei Bach b Key NQ: Western Zone SQ: Sabellaria reef extent zone MQ: Mytilus zone a. Centre of freshwater channel b. Centre of high water mark c. Boulder datum marking extent of freshwater channel at low water

Figure 3. Map of the Cei Bach intertidal boulder and cobble shore area indicating the S. alveolata reef zone (SQ), two neighbouring zones (NQ & MQ), and three environmental factor markers (a,b,c; Image: QGIS, 2010).

The community data matrix was exported from Microsoft Excel 2007, to MVSP (Kovach,

1999) and SPSS (IBM, 2011). A species list is attached and the raw data summary shows rarity and clumping amongst many of the 52 taxa present (Appendix 1 & 2).

Microsoft Excel and SPSS were used to calculate the estimated coverage (%) of S. alveolata, algae, M. edulis, and Cirrepedia in the three intertidal zones, which is illustrated in figure 4 and detailed in table 1. Zone MQ has the highest proportion of bare shore and the least in the Sabellaria zone (SQ). There is low algal coverage in the Mytilus zone (MQ), highest coverage in the Sabellaria zone (SQ) and intermediate coverage in Zone NQ.

Mytilus is absent from the Sabellaria zone (SQ) and Sabellaria rare in the Mytilus zone (MQ); with Zone NQ, intermediate for both taxa (Table 1). There is Cirrepedia coverage in Zone

NQ but rarity in Zone MQ & SQ (Fig. 4; Table 1).

N SQ 5 NQ 1 1 c 2 4 2

5 3 4 a

2 MQ 3

b 5

Key 1. S. alveolata 2. Algae 3. M. edulis 4. Cirrepedia 5. Bare shore

a. Centre of freshwater channel b. Centre of high water mark c. Boulder datum marking extent of freshwater channel at low water

NQ: Western Zone SQ: Sabellaria reef zone MQ: Mytilus zone

Figure 4. Map of the Cei Bach intertidal boulder and cobble shore area illustrating substrate coverage (%) by S. alveolata, algae, M. edulis and Cirrepedia (Table 1; Image: QGIS, 2010).

Table 1. Coverage (%) of the boulder and cobble shore intertidal zones, by S. alveolata, algae, M. edulis and Cirrepedia (Fig. 4; standard error in parenthesis).

Zone S. alveolata Algae Mytilus Cirrepedia Bare shore n MQ 0.02 (+/‐ 0.01) 5.12 (+/‐ 0.98) 11.84 (+/‐ 0.99) 0.48 (+/‐ 0.10) 82.54 300 SQ 30.93 (+/‐ 1.47) 45.46 (+/‐ 1.68) 0 0.87 (+/‐ 0.16) 22.74 600 NQ 4.97 (+/‐ 0.84) 19.79 (+/‐ 1.55) 4.86 (+/‐ 0.72) 7.67 (+/‐ 0.83) 62.71 550 1450

The process and methods for subsequent ordinations are summarised in Table 2 (Ridley et al, 2010).

Table 2. Summary of ordination methods Key: H20 = standing water

Taxa All taxa + H20 presence/absence (binary) MVSP

Analysis Cluster UPGMA PCA Sorensen’s Kaisers

Output Figs. 6 & 7

Taxa Distillation to 11 taxa + H2O

PCA Kaisers Analysis MVSP Grouping Function (by zones)

Output Fig. 8

Taxa Distillation to 7 taxa + H2O

Analysis Cluster UPGMA PCA Sorensen’s Kaisers

Analysis Cluster UPGMA Simple Matching

Output Figs. 9 & 10 (a & b)

Taxa 7 taxa - H2O Removed

Cluster UPGMA PCA Analysis Simple Matching Kaisers

Output Fig. 11 (a & b)

Analysis Direct Ordination – CCA (MVSP)

Taxa 11 key taxa Excl. H20 absence/presence binary

Environmental All Log10 transformed: 1. Shore position, 2.Stability, 3. Salinity, Variables 4. Turbulence, 5. Submersion

Analysis CCA Hills algorithmScaling by Species

Output Figs. 12, 13 (a & b), 14 (a, b, c); Table 4

Analysis CCA MVSP – Grouping function by zone (11sp.)

Output Figs. 15 & 16

Analysis Significance testing

Live S. alveolata density was highly skewed overall, rare in Zone MQ and only occasional in

Zone NQ: Zone median densities were (worms per m2); All zones, 0, 0 – 16, n = 1450: Zone

MQ, 0, 0 – 0, n = 300: Zone SQ, 24, 0 – 72, n = 600: Zone NQ, 0, 0 – 0, n = 550. SPSS was used to test S. alveolata density difference between intertidal zones; confirming significantly

2 higher density in Zone SQ, with many extremes and outliers: Kruskal-Wallis X 2 = 461.75, n1

= 300, n2 = 600, n3 = 550, P < 0.001 (Figs. 4 & 5).

n = 1450 2 m

per

density

alveolata

Figure 5. Box plot showing the significantly higher density of live S. alveolata worms in Zone

SQ, as illustrated in figure 4 (Kruskal-Wallis test).

MVSP was used to undertake indirect ordination through cluster analysis and Principal

Components Analysis (PCA) to infer community composition. Direct ordination by Canonical

Correspondence Analysis (CCA) was used to compare how well the extractions are explained by environmental factors, “species-environment correlation” (Palmer, 1993; ter

Braak, 1986 & 1994).These techniques were used successfully to explain biological assemblages and distribution of taxa, in aquatic communities, along environmental gradients by ter Braak & Verdonschot (1995).

All samples within the reef (SQ), abutting zone to the west (NQ) and abutting zone to the southeast (MQ) were tested by taxa and submersion proxy presence for inference of community structure (Fig. 3; Lesperance, 1990). Cluster analysis was utilised with UPGMA logarithm and Sorensen’s coefficient, in view of clumping and taxa rarity within the dataset

(App. 1; Krebs, 1998). Figure 6 illustrates Sorensen’s coefficient extracting two distinct clusters of 13 taxa (A & B), nesting the 3 strongest clusters; S. alveolata and Fucus vesiculosus (Ss 0.38), M. edulis and Littorina littorea (Ss 0.38), E. intestinalis and standing water (Ss 0.35). Cluster C infers rare or insignificant taxa (Fig. 6; App. 1).

taxa

edulis

standing

nesting vesiculosus nesting

M. & ; ;

F. montagui

& insignificant

& officianalis,

Ulva

C. & spiralis

(App.

littorea

nodes intestinalis and

alveolata potentially ,

, umbilicalis, S. rupestris,

N3 C. G. Porifera, pinnatifida water Cluster

N1, KEY =

N2,

of of

N1, of Rare

group group significant groups; A (6 sp.) &A B (7 significantsp.)groups; group Cluster analysisdendogram of all 52 sampled taxa indicating Cluster Cluster

= =

Cluster B A

= C grouping of rare ofgrouping or insignificant potentially intaxa two (C) and 13 taxa Figure 6.

N3 N1 N2 N3 A B 1450

n C

The cluster dendogram was copied to Microsoft Word and compared with the data output of

PCA to consider removal of insignificant or rare taxa and/or grouping of related taxa to reduce noise (Gauch, 1982; Palmer, n.d.). PCA (Kaisers rule) indicated 41 insignificant taxa, now including Porifera and Fucus spiralis, and distillation to 11 significant taxa driving

39.72% of the variance extracted by the first three axes, 1 (16.7%), 2 (12.0%) & 3 (11.0%).

The three strongest component loadings of axis 1 were examined to assess relative species importance in the extracted axis 1 (Kovach, 2011). The component loadings were compared with the cluster results and dendogram (Fig. 6). M. edulis (-0.52), E. intestinalis (0.46) and S. alveolata (0.34), were strongest and congruent with the cluster groupings and used to define the assemblage groupings A, B & C in figure 7 & subsequent figs. The insignificant taxa are distributed around the cross hairs of axes 1 and 2, with axis 1 acting as a vector separating groups B & C from group A and axis 2; group B from C (Fig. 7).

B H20

A Gw Co Me Pt Ep Cr Ul Cm KEY Pd C Me Mytilus edulis Ep Littorina littorea Bw Cm Chthamalus montagui H2OStanding water @ low tide Sa Gw Enteromorpha intestinalis Pd Osmundia pinnatifida n = 1450 Bw Fucus vesiculosus Assemblage Groupings Sa Sabellaria alveolata Pt Gibbula umbilicalis A Mytilus group Co Corallina officinalis B Enteromorpha group Cr Cladophera rupestris Ul Ulva lactuca C Sabellaria group

Figure 7. All sampled taxa PCA indicating key groupings of 11 significant taxa and standing water environmental factor with 39.72% of the variance extracted in axes 1 (16.7%); 2

(12.0%); 3 (11.0%). M. edulis, E. intestinalis and S. alveolata can be seen clearly as furthest neighbours in the scatter plot, due to the highest variance component loadings, and are used to define the three species assemblages, A, B & C respectively.

Porifera and F. spiralis were removed to refine PCA analysis with 49.81% of variance now extracted by the first three axes, 1 (21.0%), 2 (15.2%) & 3 (13.6%) and M. edulis, E. intestinalis & S. alveolata axis 1 component loadings of -0.53, 0.47 & 0.34, respectively (Fig.

8; Kovach, 2011).

The samples were grouped by the three study zones (MQ, SQ, NQ) using MVSP grouping function and combined with euclidean bi-plots to test different taxa and grouping response and vectoring with the study zones (Duigan & Kovach, 1991; Kovach, 2011). Axis 1 acted as a presence vector confirming taxa group A (M. edulis, L. littorea and Chthamalus montagui) to the left of the bi-plot, where Mytilus zone MQ dominates, and groups B & C to the right where Sabellaria zone SQ dominates; Taxa / group response to the western area NQ is not clear and considered later in a test of association (Fig. 8; ter Braak, 1987 & 1994).

Figure 8 illustrates short vectors and the lower significance of Gibbula umbilicalis,

Cladophera rupestris, Corallina officinalis and Ulva in driving variance and these were removed for subsequent and final refinement of PCA analysis with and without the submersion proxy (Palmer, n.d.; Pusceddu et al, 2011). Of the significant responses, E. intestinalis appears diametrically opposed to L. littorea and C. montagui, and S. alveolata very directionally similar to Osmundia pinnatifida and F. vesiculosus, which are identically orientated.

NQ.

the

Ulva, analysis. (21%);

to 1

zone showing

illustrate

PCA SQ

standing

and axes;

and

zone and

arrows

officinalis of

three

distributed (A,B,C) C. taxa

zone cluster

zone West first

vector

final samples evenly

the

group in rupestris,

the Mytilus and significant Zone

Sabellaria

C. more

assemblages

group for 11

group (SQ) (NQ) (MQ) of

with = = =

environmental

extracted

vector species plot

‐ left, subsequent bi umbilicalis, short Mytilus Enteromorpha Sabellaria

was

key

= the = = for

X C B A The to

presence three

MQ variance

removed

(13.6%). the

zone PCA Euclidean

response 3

8. indicating were acting

and

1 taxa

which water, 49.81% low Axis (15.2%); right Figure

B C 1450

= n A

To further resolve the whole community dendogram, the seven taxa contributing most strongly to the PCA assemblage directional vectors were used in cluster analysis with

Sorensen’s coefficient (Figs. 6 & 9). Fig. 9 illustrates the three clusters (A (Ss 0.32 ); B (Ss

0.35 ); C (Ss 0.27)) & extracted, with nested clusters within A & C. Group A; L. littorea & M. edulis (Ss0.38) and Group C; S. alveolata & F. vesiculosus (Ss 0.39).

n = 1450 A N4

C N5

B N3

N1 –5 = Cluster nodes

Figure 9. Cluster analysis and suggested assemblages (A,B,C) of seven most significant taxa and submersion proxy (H2O; Sorensen’s coefficient).

To understand taxa response, inferred by absence, to environmental factors such as salinity, desiccation, standing water (submersion), or competitive exclusion, I used simple matching coefficient cluster analysis (Little et al, 2009). Jaccards coefficient was rejected as the taxa are not evenly distributed through the dataset (Lesperance, 1990). Simple matching coefficient shows strong confidences in splitting group A (Sm 0.69) from B (Sm 0.68) & C (Sm

0.75), linking them by the submersion proxy, A(Sm 0.62) & B(Sm 0.63) and inferring nested clusters of C. montagui & M. edulis (E, Sm 0.74) within A and O. pinnatifida and F. vesiculosus (D, Sm 0.83) within C (Fig. 10(a)).

The simple matching clusters are corroborated by PCA with 59.0% of variance extracted in the first three axes, 1 (24.6%), 2 (18.5%) & 3 (15.9%), illustrated by groups A, B & C in figure

10(b).

n = 1450 A

B C D

N3 N4 N2 N1 KEY: N1 –7 Cluster nodes (a) N6 N5 N7

H20

B A Gw Me

Cm Ep Pd

Bw KEY: Me Mytilus edulis Ep Littorina littorea C Cm Chthamalus montagui Sa H O Standing water @ low tide 2 Assemblage Groupings Gw Enteromorpha intestinalis Pd Osmundia pinnatifida Bw Fucus vesiculosus A Mytilus group Sa Sabellaria alveolata B Enteromorpha group n = 1450 (b) C Sabellaria group

Figure 10. Cluster and corroborative PCA analysis of seven most significant taxa and submersion proxy, with 59% of the variance extracted in axes 1 (24.6%); 2 (18.5%); 3

(15.9%).

With the submersion proxy removed, the final taxa assemblage cluster and PCA analyses show high congruence (Fig. 11(a & b)). Cluster analysis shows, group A M. edulis similarity with C. montagui (Sm 0.74) and L. littorea (Sm 0.69); group B O. pinnatifida and F. vesiculosus similarity (Sm 0.83) and S. alveolata (Sm 0.75), connected to group C (E. intestinalis) (Sm 0.68; Fig. 11(a)). PCA corroborates groups A, B & C with 63.5% of variance extracted in the first three axes, 1 (28.4%), 2 (20.4%) & 3 (14.7%) and the three group defining taxa contributing most strongly to axis 1 with component loadings of -0.54, 0.46 &

0.44 for M. edulis, E. intestinalis and S. alveolata respectively (Fig. 11(b)). Axis 1 acts as a vector separating groups B & C from A, and axis 2, sub group Ai & C from E. intestinalis and

M. edulis (Fig. 11(b)).

n = 1450 A

N4 N2 (a) N1 KEY: N1, N2, N3, N4, N5 = cluster nodes N5 N3

Sa A Ep C

Ai Bw Cm Pd

Me B KEY: Gw Me Mytilus edulis Ep Littorina littorea n = 1450 Cm Chthamalus montagui Gw Enteromorpha intestinalis Assemblage Groupings Pd Osmundia pinnatifida Mytilus group (incl. Ai nested group) Bw Fucus vesiculosus A Sa Sabellaria alveolata B Enteromorpha group C Sabellaria group (b)

Figure 11. Cluster dendogram and PCA scatter plot of seven most significant taxa and assemblage groupings, with 63.5% of the variance extracted in axes 1 (28.4%); 2 (20.4%); 3

(14.7%). The submersion proxy was removed in this final cluster analysis and PCA.

CCA is suitable for contingency data such as taxa absence/presence and was used to directly identify taxa response to the environmental variables measured in the study

(Kovach, 2011; Palmer, 1993; ter Braak, 1986). By including only the 11 significant taxa identified by PCA as driving variance, the Correspondence Analysis (CA) tendency of distortion by outliers is avoided and CCA “downweighting” of rare species unnecessary (Fig.

7; Gauch, 1982; Kovach, 2011; ter Braak, 1994). MVSP CCA is not affected by the instability problems described by Oksanen & Minchin (1997; Kovach, 2011).

Hills algorithm and scaling by species was used in each CCA (ter Braak, 1986 & 1994).

Substrate type data was collected in nominal classes drawn from the Wentworth scale (in

Little, 2000), as a measure of stability. For CCA analysis, these nominal classes were changed to the lowest size value for each class and log transformed, in view of the non- linear nature of the classes, as discussed by Palmer (1993; Table 3). As species response curves could be affected by non-transformation of the other environmental data, all environmental variables were Log transformed using Microsoft Excel Log10 function (Palmer,

1993). Where standing water coverage was zero %; 0.001% was substituted to avoid infinity values.

Table 3. Log transformed values of Wentworth scale particle classes

Nominal Value Min. size (mm) Log 10 Boulder 256 2.41 Cobble 64 1.81 Pebble 4 0.60 Granules 2 0.30 Sand 0.063 ‐1.20

Eigenvalues (Ev) are stated for the axes to indicate ordination quality and the variance in the taxa presence data; cumulative constrained percentage (Cc) to indicate how much of the variance is explained by the constraining environmental variables; and species/environmental correlation (Se r) as a measure of how well the environmental variables explain the taxa community composition (ter Braak, 1986, 1994; ter Braak &

Verdonschot, 1995).

Correlations with the ordination axes of species or environmental variables, are not published in tables or text as they can be derived from the bi-plot figures (head of the arrow for environmental variables) e.g. Shore position (HW; 1) in fig. 12 is correlated -1.3 with axis

1 and +0.3 with axis 2, which derives the arrow length from source, at the cross hairs of axes

1 and 2 (Leps & Smilauer, 2003; ter Braak, 1994).

The MVSP graphing function was used to create bi-plots of the CCA which were illustrated in

Microsoft PowerPoint to consider corroboration of cluster and PCA assemblages and defining taxa. Environmental variable arrows were given conspicuous extensions and perpendicular lines drawn from taxa centroids to illustrate inferred taxa response ranking (ter

Braak, 1987). To avoid overcrowding in interpretation, separate illustrations of the bi-plots were illustrated for each key environmental variable (Snoeijs & Prentice, 1989).

Five environmental variables were considered, with the 11 taxa; 1. Shore position and associated desiccation (HW) i.e. distance from high water; 2. Stability (Substrate) i.e. macro- particle size class (Table 3); 3. Salinity proxy (FW) i.e. distance from centre of freshwater channel; 4. Turbulence proxy i.e. low water point of freshwater channel; 5. Submersion (SW

H20%) i.e. coverage by standing water at low tide (Figs. 11 & 12). Sand particle size was not included in CCA analysis as an environmental factor, as sand samples were not collected, or available, from all quadrat samples. Sand grain size is considered later.

The CCA direct ordination shows 68% of variance in the taxa presence data extracted in the first two axes and supports the refined cluster and PCA extracted community assemblages

(Figs. 7 & 11): Ev; Axes 1 (0.53), 2 (0.15). 77.6% of variance was explained by environmental constraint: Cc; Axes 1 (60.5), 2 (17.1; Fig. 12). Axis 1 acts strongly to explain taxa community composition and the separation of groups B & C from A, by shore position

(1; HW) and relative stability (3): Se r; Axes 1 (0.83), 2 (0.55), (Fig. 12; ter Braak 1994; ter

Braak & Verdonschot, 1995).

CCA axis 2 correlates more weakly with species assemblage but vectors environmental variance in the proxies for increasing salinity (4) and reducing turbulence (5; Fig. 12). Ulva is associated with group B (E. intestinalis) and C. officinalis with submersion (2) and group C

(S. alveolata) in axis 2, which is a transposition from their weak axis 2 vectoring in PCA & cluster analysis (Figs. 7 & 12).

n = 1235 Axis 1 r2 = 0.69 Axis 2 r2 = 0.30

B Gw A 1 Cm Ul 3 Pd Sa Me Bw Ep C Cr 2 Pt Co

KEY Me Mytilus edulis Ep Littorina littorea Assemblage groupings Cm Chthamalus montagui A = Mytilus group Gw Enteromorpha intestinalis Pd Osmundia pinnatifida Bw Fucus vesiculosus B = Enteromorpha group Sa Sabellaria alveolata Pt Gibbula umbilicalis Co Corallina officinalis C = Sabellaria group Cr Cladophera rupestris Ul Ulva lactuca

Figure 12. CCA bi-plot of 11 key taxa, their group assemblages (A,B,C), and response to environmental factors of 1. (HW) Gradient towards low water 2. (SW) Gradient of increased submersion, 3. (Substrate) Gradient towards increased abiotic stability, 4. (FW) Gradient towards increased salinity and, 5. (Bldr) Gradient towards reduced turbulence / mixing.

Variance is extracted 68% in the first two axes; 1 (53%); 2 (15%) and 77.6% explained by environmental constraint; 1 (60.5%); 2 (17.1).

Figure 13 illustrates the ranking of taxa response to environmental variables of, (a) gradient from high water to low water (position on shore) and, (b) gradient from unstable to stable available abiotic substrate; vectored most strongly by axis 1 variance extraction.

(a) Shore Position n = 1235 Axis 1 r2 = 0.69 Axis 2 r2 = 0.30

Gw 1 Cm Low Water Ul 3 Pd Me Bw Sa Ep KEY Me Mytilus edulis Cr High Water 2 Ep Littorina littorea Pt Cm Chthamalus montagui Co Gw Enteromorpha intestinalis Pd Osmundia pinnatifida 5 Bw Fucus vesiculosus 4 Sa Sabellaria alveolata Vector extension illustrations Pt Gibbula umbilicalis Co Corallina officinalis Perpendicular to environmental Cr Cladophera rupestris vector Ul Ulva lactuca

(b) Stability n = 1235 Axis 1 r2 = 0.69 Axis 2 r2 = 0.30

Gw 1 Cm Stable Ul 3 Pd Me Unstable Bw Sa Ep Cr 2 Pt Co Vector extension illustrations 2

Axis 5 Perpendicular to 4 environmental vector

Figure 13. Ranking of taxa response to environmental variables of, (a) Shore position and associated desiccation gradient from high to low water and, (b) Stability gradient from unstable to stable abiotic substrate. Axis 1 is acting as the key vector with 53% variance extracted and 15% in axis 2. 77.6% of variance is explained by environmental constraint, extracted in the first 2 axes; 1 (60.5%); 2 (17.1%).

Figure 14 illustrates the CCA ranking of taxa response to the environmental gradients of; (a) submersion, vectored by axis 1 & 2 and, (b) salinity and, (c) turbulence / mixing, both vectored by axis 2.

n = 1235 Dessicated Axis 1 r2 = 0.69 (a) Submersion Axis 2 r2 = 0.30

Gw Cm

Ul Pd Me Bw Sa Ep Cr Pt Co Vector extension illustrations 2

Submerged

Axis Perpendicular to environmental vector

Freshwater n = 1235 (b) Salinity Axis 1 r2 = 0.69 Axis 2 r2 = 0.30

Gw Cm

Ul Pd Me Bw Sa Ep Cr Pt Co Vector extension illustrations 2

Axis Perpendicular to Seawater salinity environmental vector

Turbulent / Mixed n = 1235 Axis 1 r2 = 0.69 (c) Turbulence / mixing Axis 2 r2 = 0.30

Gw Cm

Ul Pd Sa Me Bw Ep Cr Pt Co Vector extension illustrations 2

Less

Axis Perpendicular to Mixed environmental vector by river

Figure 14. CCA ranking of taxa response to environmental variables (a) submersion, vectored by axis 1 and 2, (b) salinity proxy and, (c) turbulence / mixing proxy, both vectored by the more weakly correlated axis 2. Variance is extracted 68% by axes 1 and 2; 1 (53%); 2

(15%) and 77.6% explained by environmental constraint; 1 (60.5%); 2 (17.1%).

The CCA taxa response rankings are summarised in table 4.

Table 4. Summary CCA ranking of taxa response to environmental variables, shown in

figure 13 & 14.

Axis 1Axis 2 Rank From High Water Group Stable Group Dessicated Group Freshwater Group Mixed Group Towards Low Water Unstable Submersed Saline Unmixed 1 M. edulis A M. edulis A M. edulis A M. edulis A M. edulis A 2 C. montagui A C. montagui A G. umbilicalis A C. montagui A C. montagui A 3 G. umbilicalis A G. umbilicalus A C. montagui A G. umbilicalis A L.littorea A 4 L. littorea A L. littorea A L.littorea A L. littorea A G. umbilicalis A 5 C. officinalis C C. officinalis C C. officinalis C C. officinalis C E. intestinalis B 6 S. alveolata C S. alveolata C S. alveolata C S. alveolata C S. alveolata C 7 E. intestinalis B E. intestinalis B E. intestinalis B E. intestinalis B Ulva B 8 O. pinnatifida C O. pinnatifida C C. rupestris C O. pinnatifida C O. pinnatifida C 9 F. vesiculosus C F. vesiculosus C F. vesiculosus C F. vesiculosus C F. vesiculocus C 10 C. rupestris C C. rupestris C O. pinnatifida C C. rupestris C C. officinalis C 11 Ulva B Ulva B Ulva B Ulva B C. rupestris C

Fig. PPP (a) PPP (b) NNN (a) NNN (b) NNN (c) = DIFFERENCE IN GROUP RANKING

The MVSP group function was used to express the above CCA analysis and the sample sites to consider vectoring of reef zones, which corroborated the PCA euclidean bi-plot (Figs.

8 & 15).

reef zones (MQ) to zone

Mytilus Mytilus West

to Mytilus zone Zone Sabellaria

(SQ) (NQ) (MQ) = = = (SQ) to the left, and NQ zone more evenly X CCA combinedbi-plot usinggrouping MVSP functionCCA to S. alveolata Figure 15. illustrate axis 1 acting as a vector for the the right, distributed, resolution(Fig.corroborating 8). PCA 68% of variance extracted axes; 1 (53%); by by and 77.6%explained 2 (15%) environmentalconstraint; 1 (60.5%); 2 (17.1%).

1235

n edulis

M. alveolata

S. intestinalis

2 Axis

To increase the strength of the environmental constraints and reduce the effect of intercorrelation between the salinity proxy, turbulence proxy and submersion, the latter two were removed and CCA repeated with only the three assemblage defining species (ter Braak,

1994).

The revised CCA ordination shows variance extraction reduced from 68% (Fig. 12) to 57% of variance in the taxa presence data extracted in the first two axes. This supports the refined cluster and PCA extracted dispersion of the three assemblages by these species (Figs. 11 &

16); Variance extracted by Axes; 1 (45%); 2 (12%). 100% of variance is explained by environmental constraint: Cc; Axes; 1 (78.65%), 2 (21.35%; Fig. 16). Axis 1 acts as a vector in the separation of E. intestinalis and S. alveolata from M. edulis, i.e. shore position (HW):

Se r; Axes 1 (0.68), 2 (0.41) (Fig. 16).

n = 954 Axis 1 r2 = 0.47 Axis 2 r2 = 0.17

KEY

Vector extension illustrations

Perpendicular to environmental vector

Figure 16. CCA bi-plot of three assemblage defining taxa and three environment variables,

(HW) gradient of shore position towards low water, (Substrate) gradient of increasing abiotic stability, (FW) proxy for gradient of increasing salinity by reducing exposure to freshwater.

57% of the variance is extracted by axis1 (45%) and 2 (12%). 100% of the variance was explained by environmental constraint: Axes; 1 (78.65%); 2 (21.35%).

The environmental factors inferred by CCA were tested for significance. Shore position and the proxies for salinity and turbulence were euclidean distance measurements (m) from the quadrats. Each sub cell of any given quadrat was allocated the same distance value on the environmental gradient, because the distance between the furthest cells in a quadrat was within the GPS accuracy specification (Garmin, 2012). As quantitative abundance data were not available, occurrence density was calculated from sub sample presence within each quadrat as a rapid assessment proxy for abundance (Kent, 2012; Ramsay, 2006).

The covariance between taxa occurrence density and the environmental variables of shore position, salinity proxy and turbulence proxy was tested by non parametric Spearman correlation (SPSS); as the taxa distribution was skewed (Hawkins, 2009). This confirmed strong significance, corroborating the CCA inference of the opposing taxa response of S. alveolata (Sa) and M. edulis (Me), to the shore position gradient, with the latter showing increasing occurrence density towards the more desiccated environment higher on the shore

(Figs. 13(a) & 17(a, b) : Sa, rs = 0.399, N = 58, P = 0.002; Me, rs = -0.601, N = 58, P <

0.001.

2 2 m

per

N = 58 N = 58 2 density 2

r = 0.36 r = 0.16 density

occurrence

edulis

alveolata

M. S.

(a) Distance to high water (m) (b) Distance to high water (m)

Figure 17. The significant and opposed Spearman correlations of S. alveolata (a) and M. edulis (b) to shore position with M. edulis occurring in greater density in more desiccated conditions closer to high water; corroborating CCA inference of taxa ranking (Fig. 13(a)).

The Spearman correlation suggests the inferred CCA covariance of E. intestinalis and shore position is non-significant: rs = 0.230, N = 58, P = 0.083 (Figs. 13(a); 18) 2 m

per

N = 58 density r2 = 0.05 occurrence

intestinalis

Distance to high water (m)

Figure 18. Non-significant relationship between E. intestinalis and shore position gradient

(Fig. 13(a)).

S. alveolata showed significant increasing occurrence density as the distance from the freshwater channel increased, corroborating the CCA inferred response to reduced salinity

(Fig. 14(b)): rs = 0.439, N = 58, P = 0.001 (Fig. 19).

N = 58

2 r2 = 0.19 m

per

density

occurrence

alveolata

Distance to freshwater channel (m)

Figure 19. Significant increasing S. alveolata occurrence density as the distance from the freshwater channel increases, corroborating CCA taxa response (Fig. 14(b)).

Covariance with distance from the freshwater channel and E. intestinalis (1) and M. edulis

(2) occurrence density was insignificant, corroborating the E. intestinalis position at the source point of this environmental variable in CCA figure 14(b) and inferring weak taxa response to salinity gradient Fig. 20(a,b): (1) rs = -0.137, N = 58, P = 0.304; (2) rs = 0.167, N

= 58, P = 0.167. 2 m 2

m N = 58 per 2

per r = 0.03

N = 58 r2 = 0.02 density

density

occurrence

edulis

intestinalis

M. E.

(a) Distance to freshwater channel (m) (b) Distance to freshwater channel (m)

Figure 20. Insignificant covariance of E. intestinalis (a) and M. edulis (b) occurrence density with distance from the freshwater channel (Fig. 14(b)).

Spearman correlation confirmed similarly significant covariance between S. alveolata (1) and

M. edulis (2; Fig. 21(a, b)) i.e. increased occurrence density with distance increase from the turbulence proxy which supports the close perpendiculars either side of axis 2 in the CCA taxa response ranking illustrated in figure 21(c): (1) rs = 0.285, N = 58, P = 0.03; (2) rs =

0.279, N = 58, P = 0.03 (Fig. 21). 2 2 m

N = 58 m r2 = 0.08 per

per N = 58 r2 = 0.08 density

density

occurrence

edulis

alveolata

M. S.

(b) (a) Distance to turbulence proxy datum (m) Distance to turbulence proxy datum (m)

Figure 21. Significant increase in occurrence density of S. alveolata (a) and M. edulis (b) with increasing distance from the turbulence proxy datum, corroborating CCA taxa response

(Fig. 14(c)).

E. intestinalis showed non-significant covariance with the turbulence proxy: rs = -0.115, N =

58, P = 0.39 (Fig. 14(c) & 22). 2 m

per

N = 58 r2 = 0.01 density

occurrence

intestinalis

Distance to turbulence proxy datum (m)

Figure 22. Non-significant covariance between E. intestinalis and the turbulence proxy (Fig.

14(c)).

Unlike the three environmental variables tested by correlation, the data for stability and submersion were collected to sub sample level. Therefore, a more expeditious method was used in testing significance. Stability was tested for association with the three zones and submersion for difference between the zones; and the zones tested for taxa association.

To consider stability association with the zones, the frequency distribution of samples by

Wentworth scale category (Table 3; Little, 2000) was tested with two-way Chi square

(SPSS). There was only one granule sample (Zone SQ), representing 0.07% of the total sample, and this was removed to maintain the integrity of the test (Hawkins, 2009).

Association was significant and corroborated CCA axis 1 vectoring of M. edulis (dominant in

Zone MQ; Fig. 27), with more stable substrate, away from S. alveolata (dominant in Zone

2 SQ; Fig. 27); X 6 = 210.13, N = 1449, P < 0.001 (Figs. 12 & 13(b)).

The median and upper quartile substrate classifications were pebble and cobble, respectively, in all zones; but in Zone SQ & NQ sand was the lower quartile value compared to more stable cobble in Zone MQ (all values mm): Zone MQ; 3.98, 3.98 – 64.57, n = 300:

Zone SQ; 3.98, 0.063 – 64.57, n = 600: Zone NQ; 3.98, 0.063 – 64.57, n = 550 (Fig. 12).

Whilst there are boulders in Zone SQ and NQ, instability is derived from the higher sand fraction of Zone SQ & NQ. One-way Chi square (SPSS) testing of Zones SQ & NQ only,

2 show significant heterogeneity, due to the higher sand and boulder content of Zone NQ; X 3

= 47.39, N = 1149, P < 0.001 (Fig. 13(b) & 23).

Stability

n= 300 n= 599* n= 550 Sand Sand Sand

Pebble Pebble Pebble Type

Substrate

% Cobble Cobble Cobble

Boulder Boulder

Zone * 1 sample removed (granule) from statistical test; representing 0.07% of total sample Figure 23. Sample distribution by zone and substrate type, illustrating the stability gradient and corroborating CCA taxa response of M. edulis and S. alveolata inferred by zone association (Fig. 13(b) & 27).

A Kruskal – Wallis non-parametric test was used to test the significance of difference in the submersion extent of samples in each zone, in view of skewed data; before testing taxa association with zones. The submersion median was; 0, 0 – 0.10, N = 1450 and there was

2 no significant difference between zones; X 2 = 2.359, n1 = 300 , n2 = 600, n3 = 550, P =

0.307 (Fig. 14 (a) & 24).

n = 300 n = 600 n = 550 decimal

extent

Submersion

Zone

Figure 24. Submersion extent by zone showing non – significant difference between zones and highly skewed data with many extremes and outliers (14(a)).

The sand samples were prepared for analysis to consider grain size distribution at each zone and associated taxa. Each sample of 70g – 270g was dried in a Thermo Scientific

Heratherm oven at 500C, on low fan, for 4 days and de-aggregated with a stainless steel spatula. The samples were dry-sieved using a Fritch Analysette Type 03.502 Ro-Tap shaker at amplitude seven for five minutes duration per sample (Wentworth, 1922). Sieve size selection is shown in table 5. The sand fractions were weighed and cumulative mass retained by each fraction analysed using Microsoft Excel (Fig. 25).

Table 5. Sieve selection and phi value for Ro-Tap analysis of sand samples

Sieve No Size (qm) Phi value 1 500 1.00 2 355 1.49 3 250 2.00 4 180 2.47 5 125 3.00 6 P 3.99

The median phi value at 50% cumulative distribution was 2 (medium sand) in all three zones and indicates that all three zones were well sorted, with the samples from Zone SQ & NQ slightly skewed towards smaller particles at the upper quartile (Fig. 25; Wentworth, 1922).

The sample size was too small, and expected cell values too low, to statistically test independence of the distribution by zone.

Phi value Phi value Larger Grain size Smaller Larger Grain size Smaller

Figure 25. Median % of Phi fraction mass (left) and cumulative % of fraction mass (right); illustrating sample particle size distribution by intertidal zone. (MQ, n = 6; SQ, n = 11, NQ, n

= 11). 38

Two way Chi-square (SPSS) was used to test the association between the 11 key taxa and the three intertidal zones, inferred by PCA & CCA (Figs. 4, 8 & 15). Significant association

2 between the taxa and distinct zones was confirmed; X 20 = 1516.32, N = 2518, P < 0.001

(Fig. 26). Zone MQ showed highest presence frequency of M. edulis, L. littorea and G. umbilicalus: Zone SQ; S. alveolata, E. intestinalis and F. vesiculosus: Zone NQ; C. montagui, E. intestinalis and L. littorea (Fig. 26). The higher frequency of C. montagui in

2 Zone NQ, was also significant (one way Chi-Square (SPSS)); X 2 = 63.86, N = 268, P <

0.001 (Figs. 4, 8, 15 & 26). Zones MQ and SQ assemblages in figure 26 are similar to those inferred by the final cluster analysis resolution (Fig. 11).

n = 2518 Zone MQ Zone SQ Zone NQ count

Presence

0 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Taxa: 1. E. intestinalis 2. S. alveolata 3. M. edulis 4. F. vesiculosus 5. Ulva 6. O. pinnatifida

7. C. officinalis 8. C. rupestris 9. L. littorea 10. G. umbilicalis 11. C. montagui

Figure 26. Presence frequency of the 11 key taxa at the three intertidal zones, illustrating heterogeneity in community structure. “N” is greater than total sample size (1450) as numerous taxa may be present in each sample (Figs. 4, 8 & 15).

Association between the zones and the three assemblage defining taxa, E. intestinalis, M. edulis and S. alveolata, was tested by two way Chi square (SPSS). This showed significant

2 association; X 4 = 783.76, N = 1078, P < 0.001 (Figs. 4, 8, 15 & 27). This supports the PCA opposed presence vectoring of S. alveolata to M. edulis (Zones MQ & SQ); but not Zone

NQ, as E. intestinalis exhibits high presence frequency in both Zone SQ & NQ, the latter of which has intermediate presence of the three taxa, when compared to Zones MQ & SQ

(Figs. 4, 8, 15 & 27).

n= 1078 KEY:

2 1. E. intestinalis

2. S. alveolata

3. M. edulis 3 1 count

Presence 1

3 2 1 2 3

Figure 27. Significant association between zones MQ & SQ with M. edulis and S. alveolata and high E. intestinalis presence in zones SQ & the intermediate zone, NQ, corroborating

PCA & CCA inferences (Figs. 4, 8 & 15).

As CCA inferred shore position as the environmental factor most correlated with taxa response, shore position was tested for difference by zone (Fig. 13(a)). As the distances were normally distributed, parametric testing was suitable and most powerful (Hawkins,

2009). This was significant (One-way Anova (SPSS)); F2, 1447 = 836.47, P< 0.001, N = 1450 and corroborates the taxa response ranking to shore position, inferred by CCA, of S. alveolata and M. edulis (by zone association; Figs. 13(a) & 27). Mean distance (m) from high water for Zone MQ, associated with Mytilus, was; 54.72, +/- 0.45, n = 300: Zone SQ, associated with Sabellaria; 119.02, +/- 1.13, n = 600: Zone NQ, intermediate taxa association; 104.05, +/- 0.91, n = 550 (Fig. 28).

N = 1450 CI = 95% ) (m

water

high

Distance

Figure 28. Significant difference in mean distance from high water in the samples of the three intertidal zones (One-way Anova; Figs. 13(a) & 27).

DISCUSSION

The boulder and cobble shore at Cei Bach exhibited distinct community assemblage patterns within the reef, to the west and to the south-east, as hypothesised (Fig. 26). Cluster analysis and PCA were highly corroborative in distilling the overall community of 52 taxa to the seven key species that were driving the greatest variance, and assemblage distinction

(App. 1; Figs. 6 & 11(a & b)). It was also confirmed that two zones could be associated with assemblage group defining taxa; Zone SQ with S. alveolata and Zone MQ with M. edulis; but, the defining taxa of the third assemblage, E. intestinalis, could not be distinctly zone associated as it was present in Zone MQ and populated zones SQ and NQ with high frequency, inferring a broader fundamental niche than two other taxa (Figs. 11(a & b), 27;

Begon et al, 2006).

The EU Habitat Directive designation of the S. alveolata biotope recognises the high biodiversity supported by this ecosystem engineer, and the higher taxa presence frequency in Zone SQ, compared to both other zones, and higher species richness than the Mytilus zone, indicates higher biodiversity in the reef (Figs. 4 & 26; Table 1; Maddock, 2008). In view of the limitations of the abundance proxy, more refined data are required to formally quantify this proposition, e.g. Shannon-Wiener index (Espinosa & Guerra-Garcia, 2005; Ramsay,

2006). Similar species richness in zones SQ and NQ, lower bare shore coverage in zone SQ and intermediate taxa frequency in Zone NQ, suggest a biodiversity gradient decreasing through zones, SQ, NQ and MQ (Figs. 4 & 26; Table 1; Park, 2008).

PCA inferred similar axis 1 vectoring of group B (E. intestinalis) and C (S. alveolata), driven by common presence in zones SQ and NQ, and similar lower shore position (Figs. 8, 27 &

28). CCA corroborated this as a strong response to the shore position gradient and vectored group A (M. edulis) opposite groups B and C, which are closer to low water resulting in increased periods of submersion; identical resolution to the simple matching cluster dendogram (Fig. 11, 15 & 28; Leps & Smilauer, 2003). Response to the CCA submersion

42 gradient illustrated in figure 14(a) is not discussed further as it is highly intercorrelated with shore position and proximity to the freshwater channel, and non-significant (Fig. 24; Leps &

Smilauer, 2003). However, this was resolved in cluster analysis and corroborated with PCA by treating standing water as a species, for analysis purposes and confirms the association of E. intestinalis, O. pinnatifida, F. vesiculosus and S. alveolata with higher submersion (Fig.

10).

The broad fundamental niche explanation of substantial E. intestinalis presence in zones SQ and NQ is supported by its competitive ability, nutrient uptake efficiency, euryhalinity and high tolerance of nutrient load (Fig. 27; Zhang, 2012). Superior net primary productivity of E. intestinalis, similar to terrestrial grass as discussed by Silvertown et al (2006), enables it to characteristically outcompete other taxa when nitrate and light is available in salt, fresh or brackish conditions (Hutchinson, 1960; MarLIN, 2013; Smith et al, 2006; Zhang et al 2012).

Zhang (2012) undertook genetic studies of Ulva linza, a congener of E. intestinalis, and concluded that early evolution and adaptation of mechanisms including carbon concentrating proteins, similar to C-4 plants, and heat-shock proteins facilitated rapid radiation and successful colonization of highly stressed coastal ecosystems. Other studies have shown strong functional guilds among C-4 terrestrial grass species, and in this study, Ulva lactuca, demonstrated similar variance to E. intestinalis when environmentally constrained, which was not as clearly extracted in the earlier PCA analysis (Figs. 7 & 12; Fargione et al, 2003).

The highly efficient net primary productivity and simple thalli solution of E. intestinalis, that absorbs and processes dissolved nutrients, contrasts with the relatively complex biology and filter feeding mechanisms of M. edulis and S. alveolata, which suffer impaired feeding efficiency in high seston concentration and showed reduced occurrence as turbulence increased (Figs. 14(c); 21(a, b & c); Dubois et al, 2005 & 2009; Seed & Suchanek, 1992;

Zhang, 2012).

E. intestinalis and other Ulva sp. are widely used to indicate fresh water incursion and eutrophication (Raffaelli et al, 2004; Worm et al, 2000). The Environment Agency (EA)

43 classifies nutrient loading of UK rivers (1 – 6), with class five and six being high and very high respectively. Agricultural pollution from freshwater run-off can alter community structure at the intertidal interface but high or very high EA nitrate classification levels are rare in

Welsh rivers, compared to 32% in England, suggesting that the saline diluting effect of the river may be more important than allochthonous nitrate loading of the study area (EA, 2013a;

Hack et al, 2009; Pusceddu et al, 2011). However, 2008 statistics are the most recent EA nitrate data published and exceptionally high rainfall levels in 2012 may have increased agricultural run-off and eutrophication levels (Dodds, 2006; Little et al, 2009; MetOffice,

2013).

The land draining into the Llethi river basin is not designated a Nitrate Vulnerable Zone under the Nitrate Pollution Prevention Regulations 2008 (as amended 2013), but these regulations are being phased in with effect from May 2013, revised designations will be issued shortly and the sensitivity of the S. alveolata reef within the SAC could result in designation and increased protection from nutrient perturbation (Defra, 2013; EA, 2013b).

Figure 11 illustrates cluster and PCA axis 2 vectoring of group B (E. intestinalis) from C (S. alveolata) and corroborative CCA infers stronger response to the salinity gradient by S. alveolata (Figs. 12; 14(b)), despite similar shore position response between the taxa (Fig.

13(a); axis 1). Non-significant covariance with the freshwater channel infers E. intestinalis euryhalinity and in figure 14(b) the species is located very close to the source of the gradient arrow of environmental change (Fig. 20(a)). In contrast, S. alveolata occurrence density reduces closer to the freshwater channel suggesting that the reef extent is limited to the west by reduced salinity or competitive exclusion (Figs. 4 & 19; MarLIN, 2012; Smith et al, 2006;

Townsend et al, 2008).

It would be difficult to determine the relative impact of E. intestinalis competition and reduced salinity on the S. alveolata population by inter-seasonal study of the reef boundary alone, as

E. intestinalis dies back in late summer when terrestrial freshwater run-off and flow rates also decline (EA, 2013a; Worm et al, 2000). Mesocosm exclusion experiments would be an

44 experimental design successfully tested in other investigations (O’ Gorman et al, 2010).

Such further studies could investigate switching between top-down and bottom-up pressures altering the community structure at that time (Menge, 2000). Also, mobile L. littorea may migrate further down the shore to the S. alveolata assemblage, with which it is often associated, to breed and forage fucoids as they start their nutritious growth season (Fig. 11;

Campbell, 2004; MarLIN, 2012).

Mytilus is absent from the reef (SQ), where S. alveolata live worm density is highest, but dominates Zone MQ, which has very low presence frequency of S. alveolata, algae,

Cirrepedia or other taxa (Figs. 4, 5, 26 & 27; Table 1). M. edulis is euryhaline and more tolerant of desiccation than many other biogenic reef species, and Kautsky (1982) found

0 dwarf specimens populating inner Baltic habitats of salinity as low as 4-5 /00 (Seed &

Suchanek, 1992). CCA ranked M. edulis response to the reducing salinity gradient far lower than S. alveolata, and Mytilus covariance with the salinity proxy was non-significant (Figs.

14(b), 19 & 20(b)). Temperature data were not collected in this study, but M. edulis tolerates the extremes associated with upper shore exposure, by using adapted nucleating agents in the haemolymph (Bourget, 1983). The environmental stress of the upper shore is within the broad fundamental niche of M. edulis, reflected in shore position response highly variant to

S. alveolata and E. intestinalis, and the strong axis 1 vectoring of group A from B and C

(Figs. 11 & 13(a); Little et al, 2009; UK Marine, n.d.).

The broad fundamental niche of M. edulis suggests that other factors explain its absence in

Zone SQ, such as substratum stability, particle availability and size, and competition (Figs. 4;

MarLIN, 2012; Seed & Suchanek, 1992). The response to the substrate gradient was vectored by CCA axis 1, and M. edulis was ranked with increased stability compared to E. intestinalis and S. alveolata positioned together, with lower stability (Fig. 13(b)). Substratum stability plays a key role in dispersal of aquatic macroinvertebrates and the frequency distribution, shown in figures 26 & 27, support Mytilus responding positively to stability and

S. alveolata and E. intestinalis, to the more unstable zones with higher sand fractions (Table.

1; Fig. 23; Elsasser, 2013; Hussain & Pandit, 2012). Adult M. edulis require highly stable

45 substratum for successful settlement and the community structure patterns support this (Fig.

23; Albrecht, 1998; Elsasser, 2013; UK Marine, n.d.).

S. alveolata ecosystem engineering subsequently stabilises mobile substratum within the optimal niche settlement range of M. edulis (Dubois et al, 2006; Maddock, 2008). However, studies in Mont St. Michel, France, including Desroy et al (2011), have shown that in optimal niche conditions the honeycomb worm is able to competitively exclude M. edulis and

Crassostrea gigas; and succession only occurs where the reef is seriously impaired or damaged by anthropogenic trampling. Dubois et al (2006) found that where the cultivated bivalve, C. gigas, density was lowest, S. alveolata reef health and associated species richness was highest, concluding that oysters had greater impact on reef species assemblages than algal epibionts, and a similar pattern is seen between M. edulis and the reef in this study (Fig. 26).

As M. edulis presence is frequent either side of the reef, relative specific larval dispersal is not discussed further, other than that this bi-modal distribution supports the theory of competitive exclusion of Mytilus by S. alveolata and findings of Desroy et al (2011; Blythe &

Pineda, 2009; Fig. 26). Desroy et al (2011) also found that biodeposits of M. edulis covered settling sporelings of F. serratus, inhibiting growth and new settlement, which is consistent with the very low fucoid coverage of Zone MQ (Fig. 4; Table 1; Desroy et al, 2011; Little,

2000). Whilst M. edulis is associated with lower biodiversity in this study, mussels can also act as a foundation species in other ecological conditions and provide substratum and facilitation of increased biodiversity; for example Modiolus modilous in Strangford Loch,

Northern Ireland (Fig. 26; Elsasser et al, 2013).

Sand grain availability is critical to tube building macroinvertebrates, such as freshwater cased caddis flies (Trichoptera) and marine Sabellaria (Dubois et al, 2005; Hussain &

Pandit, 2012). As there was only a small difference in sand particle size between the zones it is likely that availability is the stronger limiting factor, and figure 23 illustrates significant

46 heterogeneity, and the larger sand fraction of zones SQ and NQ, offering a plausible explanation for higher occurrence density of S. alveolata in these zones (Fig. 25 & 27).

Grain suspension is also important to the ecology of M. edulis and S. alveolata and studies have concluded optimal ranges in seston concentration, beyond which clearance rates decline and fitness is impaired, which is supported by significant density occurrence increase in both species with distance from the turbulence proxy (Fig. 14(c), 21(a & b); Dubois et al,

2003 & 2009). However, both species do require water flow from currents and wave action for particulate re-suspension, activation of feeding mechanisms and S. alveolata grain recruitment (Dubois et al, 2005). Therefore, the simplistic turbulence proxy should be treated with a caveat of caution.

S. alveolata is often associated with F. serratus which exploits reef substrate for settlement and development of sporelings, similarly to the settlement and metamorphosis of new generations of meroplanktonic trochophores of S. alveolata (MarLIN, 2012; Pawlik, 1988). In this study F. serratus is replaced with F. vesiculosus, which is an indicator of more sheltered shores (Figs. 11, 12 & 26; Little, 2000). Both S. alveolata and F. vesiculosus are considered foundation species, providing increased three dimensional substrate and surface area for other taxa, increasing biodiversity in a similar way to terrestrial forest canopy (Diaz et al,

2012; Little et al, 2009; Maddock, 2008). The relationship between the F. vesiculosus and S. alveolata is mutualistic, as the fucoid provides a desiccation buffer at low tide without impairing filter feeding upon tidal inundation as it re-floats vertically (Little et al, 2009).

This study is a “snapshot” resolving and focused upon the assemblage defining taxa of the boulder and cobble intertidal community, which is dynamic, temporally affected and vulnerable to extreme perturbations (Bertocci et al, 2012; Lancaster & Savage, 2008;

Wethey et al, 2011).

CONCLUSIONS

The indirect and direct ordination techniques used to analyse the boulder and cobble shore at Cei Bach were effective in supporting all study objectives and hypotheses, other than the key assemblage defining species, E. intestinalis, was not zone associated due to broad fundamental niche and competitive superiority in the absence of limiting factors (Zhang,

2012).

Increased biodiversity was associated with the biogenic reef and the results, and literature review, suggest that its dynamic extent is effectively “framed” by; the open sea to the North; desiccation to the south; insufficient salinity, high seston levels and competitive exclusion, by

E. intestinalis, to the west and; desiccation, and possibly competitive exclusion by M. edulis, to the south-east (Fig. 4; Lancaster & Savage, 2008; Maddock, 2008). Therefore, the relative coverage and abundance of M. edulis, E. intestinalis and S. alveolata could be considered useful bio-indicators for long term reef health and succession monitoring (Desroy et al, 2011;

Dubois et al, 2006; UK Marine, n.d.). There is evidence of facilitation between S. alveolata and F. vesiculosus and functional guild relationship between Ulva sp. (Fig. 26; Fargione et al, 2003; Little et al, 2009).

As we progress from a species and habitat focus towards an ecosystem approach to conservation, the techniques used in this study may be adapted and improved in assessing the ongoing ecological status, succession and reference conditions at Cei Bach, and similar habitats within a landscape and river basin scale, according to the European Water

Framework Directive (EA, 2013a, EAW, 2012; Maddock, 2008; Muxika et al, 2007).

CRITIQUE

By combining cluster and PCA, rare taxa and those driving low variance were excluded and the main criticisms of Correspondence Analysis (CA) mitigated, thus enabling robust CCA direct ordinations (Gauch, 1982; Palmer, n.d.). However, these taxa should be considered in further studies, as they are important in complex food webs and community structure

(O’Gorman & Emmerson, 2009; Pusceddu et al, 2011).

For expediency, and as CCA design is suitable for contingency data (presence/absence), these were used as an efficient alternative to abundance data, but the accuracy of this surrogate approach has not been tested in this study (Kovach, 2011; Ramsay 2006).

CCA inferred a broader picture of taxa ranking and community response, to multiple environmental factors, than individual linear regressions (Kovach, 2011). However, CCA relies on the skills and experience of the ecologist to select the most appropriate environmental variables and there was, perhaps inevitably, some intercorrelation between shore position, submersion and proxies for turbulence and salinity which reduced correlation strength, particularly in axis 2 (Fig. 12; ter Braak, 1986 &1994). This is a challenge for most studies of complex intertidal communities and more advanced methods continue to be developed (O’ Gorman et al, 2010).

The salinity gradient proxy of distance from the freshwater channel is relevant only to low tide conditions, as freshwater stratifies above more dense seawater on tidal inundation, but remains a good indicator of niche tolerance to daily salinity extremes (Little et al, 2009). The turbulence proxy is supported by peer reviewed studies of increased seston proximate to freshwater outfall, but no corroborating measurements were taken in this study (Aller &

Cochran, 1976; Sebens et al, 1997).

My study was very ambitious and, with hindsight, I collected too much data of which some were not salient, increasing data analysis complexity and reducing efficiency. The mantra,

“keep it simple” would have facilitated faster results and I may have spotted omissions and errors more quickly. For example, temperature data could have been interesting and coverage data for E. intestinalis would have been useful in the analysis of taxa response of the three defining species, and as a bio-indicator of eutrophication (Worm et al, 2000;

Zhang, 2012). Selection of alternative software, such as Supervised Multidimensional

Scaling (superMDS) in “R”, may also have reduced my ordination process to fewer steps, improved efficiency and inferred further interesting variances (Cran, 2013).

With increased funding, GPS sample location accuracy could have been improved to 3 – 5m with the use of a supplemental Differential Beacon Receiver (GPS Information, 2013).

ACKNOWLEDGEMENTS

I would like to thank, Dr. Philip Pugh (my supervisor), Cardigan Bay Marine Wildlife Centre

(CBMWC) - Laura Mears, Sarah Perry, Steve Hartley, Matt Jones and my other co- volunteers for their support and encouragement, The Countryside Council for Wales (CCW)

– Paul Brazier, Julia Mackenzie and Jacqueline Bodimead at Anglia Ruskin University (ARU) and Roger & Bethan Bryan for their hospitality and generous car parking at Cei Bach.

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Appendices

Appendix 1. Species list of taxa present in study

Fucus vesiculosus

F. serratus

F. spiralis

Ascophyllum nodosum

Gibbula umbilicalis

Pelvetia canaliculata

Gelidium latifolium

Porphyra umbilicalis

Ulva lactuca

Laminara digitata

Leathesia diffromis

Osmundia pinnatifida

Palmaria pulmata

Osmundia obtuse

Chondrus crispus

Mastocarpus

Corallina officinalis

Other coralline algae

Lomentaria articulate

Cladophera rupestris

Enteromorpha intestinalis

D. contorta

Codium tomentosum

Sargassum muticum

Laminaria saccharina

Osmundia obtuse

Chaetomorpha linum

Dictyota sp.

Sabellaria alveolata (Live)

S. alveolata (Dead)

Mytilus edulis

Littorina littoralis

Littorina littorea

Littorina saxtillis

G. cineraria

G. umbilicalis

Cardium edule

Chthalamus montagui

Semibalanus balanoides

Elminius modestus

Nucella lapillus

Arenicola marinara

Pomotocerus lamarki

Spirorbis spirorbis

Porifera sp.

Decapoda

Pagurids

Aquina equine

Crangon vulgaris

Gigas sp.

Patella vulgaris

Chiton sp.