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

1

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).

N

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).

2

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.

4

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).

5

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

a

b

b

MQ

SQ

NQ

KeyNQ: Western Zone SQ: Sabellaria reef extent zone MQ: Mytilus zonea. Centre of freshwater channelb.  Centre of high water markc.  Boulder datum marking extent of freshwater channel at low water

11

3

3

c

Cei Bach

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).

7

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

a

b

b

MQ

SQNQ

Key1. S. alveolata 2. Algae  3. M. edulis 4. Cirrepedia 5. Bare shore

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

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

5

5

51

1

3

2

2

3

4

4 2c

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 nMQ 0.02 (+/‐ 0.01) 5.12 (+/‐ 0.98) 11.84 (+/‐ 0.99) 0.48 (+/‐ 0.10) 82.54 300SQ 30.93 (+/‐ 1.47) 45.46 (+/‐ 1.68) 0 0.87 (+/‐ 0.16) 22.74 600NQ 4.97 (+/‐ 0.84) 19.79 (+/‐ 1.55) 4.86 (+/‐ 0.72) 7.67 (+/‐ 0.83) 62.71 550

1450

8

The process and methods for subsequent ordinations are summarised in Table 2 (Ridley et

al, 2010).

9

Table 2. Summary of ordination methods

Key: H20 = standing water

All taxa + H20 presence/absence (binary) MVSP

Cluster UPGMASorensen’s

Taxa

Analysis PCAKaisers

Figs. 6 & 7Output

Distillation to 11 taxa + H2OTaxa

PCA KaisersMVSP Grouping Function (by zones)Analysis

Fig. 8Output

Distillation to 7 taxa + H2OTaxa

Cluster UPGMASorensen’s

PCAKaisers

Analysis

Cluster UPGMASimple Matching

Analysis

Figs. 9 & 10 (a & b)Output

7 taxa - H2O RemovedTaxa

Cluster UPGMASimple MatchingAnalysis

Fig. 11 (a & b)Output

Analysis

PCAKaisers

Direct Ordination – CCA (MVSP)

11 key taxa Excl. H20 absence/presence binaryTaxa

EnvironmentalVariables

CCA Hills algorithm Scaling by SpeciesAnalysis

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

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

Output Figs. 15 & 16

Significance testing

Analysis

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

10

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

higher density in Zone SQ, with many extremes and outliers: Kruskal-Wallis X22 = 461.75, n1

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

S. alveo

lata den

sity per m

2

n = 1450

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).

11

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).

12

KEY

N1, N2, N3 = Cluster no

des

A = Cluster group

 of 

G. umbilicalis, C. m

ontagu

i, Po

rifera, F. spiralis; ne

sting 

N2, L. littorea & M

. edu

lis

B= Cluster grou

p of

C. ru

pestris, C. officiana

lis, O

. pinn

atifida

 & Ulva; nestin

g N3, E. intestina

lis & stand

ing 

water and

N1, S. a

lveo

lata & F. vesiculosus

C= Cluster grou

p of

Rare or po

tentially insignificant taxa 

(App

. 1)

Figu

re6.

Clu

ster

ana

lysi

s de

ndog

ram

of a

ll 52

sam

pled

taxa

indi

catin

g gr

oupi

ng o

f rar

e or

insi

gnifi

cant

taxa

(C) a

nd 1

3 ta

xa in

two

pote

ntia

lly

sign

ifica

nt g

roup

s; A

(6 s

p.) &

B (7

sp.

)

13

C

A B

n = 14

50N1 N2

N3N3

14

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).

Me

Pd

EpCm

Gw

H20

Sa

Bw

KEY

Me Mytilus edulisEp Littorina littoreaCm Chthamalus montaguiH2O Standing water @ low tideGw Enteromorpha intestinalisPd Osmundia pinnatifidaBw Fucus vesiculosusSa Sabellaria alveolataPt Gibbula umbilicalisCo Corallina officinalisCr Cladophera rupestrisUl Ulva lactuca

Pt

Cr Ul

Co

C

B

A

Assemblage Groupings

CBA

Enteromorpha group

Mytilus group

Sabellaria group

n = 1450

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.

15

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.

16

X= (M

Q)M

ytilus zone

= (SQ)Sab

ellaria zone

= (NQ) Zon

e to W

est

A = M

ytilusgrou

pB = Enteromorph

agrou

pC = Sabellariagrou

p

Figu

re 8. PCA

Euclidean bi‐plot of 11 significant taxa and standing

 water, ind

icating three key species assemblages (A,B,C) and

 sho

wing 

Axis 1 actin

g as a presence vector fo

r the samples of zon

e SQ

 to th

e right and zone

 MQ to

 the left, w

ith m

ore evenly distributed

 zon

e NQ. 

49.81%

 of the

 variance was extracted

  in the first th

ree axes; 1

 (21%

); 2 

(15.2%

); 3 (1

3.6%

). The short environm

ental vector arrows illustrate 

low taxa respo

nse of G. umbilicalis, C. rup

estris, C. officinalis & Ulva, 

which were removed

 for subseque

nt and

 final cluster and

 PCA

 analysis.

17

A

C

B

n = 14

50

18

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).

A

B

C

N4

N2

N1

N5

N3

n = 1450

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)).

19

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).

(a)

n = 1450A

B

E

C

D

N4N3

N5

N2 N1

N7N6

KEY: N1 – 7 Cluster nodes

KEY:Me Mytilus edulisEp Littorina littoreaCm Chthamalus montaguiH2O Standing water @ low tideGw Enteromorpha intestinalisPd Osmundia pinnatifidaBw Fucus vesiculosusSa Sabellaria alveolata

Me

Pd

Gw

H20

Cm Ep

Sa

Bw

AB

C

Assemblage Groupings

Mytilus group

Enteromorpha group

Sabellaria group

ABC

n = 1450(b)

20

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)).

21

n = 1450

KEY: N1, N2, N3, N4, N5 = cluster nodesN1N2N4

N3N5

(a)

A

C

D

B

E

n = 1450

Bw

Me

Cm

Ep

Pd

Gw

Sa

KEY:Me Mytilus edulisEp Littorina littoreaCm Chthamalus montaguiGw Enteromorpha intestinalisPd Osmundia pinnatifidaBw Fucus vesiculosusSa Sabellaria alveolata

Assemblage Groupings

ABC

Mytilus group (incl. Ai nested group)

Enteromorpha group

Sabellaria group

A

B

C

Ai

(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.

22

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 10Boulder 256 2.41Cobble 64 1.81Pebble 4 0.60Granules 2 0.30Sand 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).

23

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).

24

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).

Cm

Ul

BwPd

Gw

Sa

Cr

CoPt

MeEp

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

1

54

3

2

AB

C

KEYMe Mytilus edulisEp Littorina littoreaCm Chthamalus montaguiGw Enteromorpha intestinalisPd Osmundia pinnatifidaBw Fucus vesiculosusSa Sabellaria alveolataPt Gibbula umbilicalisCo Corallina officinalisCr Cladophera rupestrisUl Ulva lactuca

Assemblage groupings A = Mytilus group

B = Enteromorpha group

C = Sabellaria group

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).

25

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.

Cm

Ul

BwPd

Gw

Sa

Cr

CoPt

MeEpKEY

Me Mytilus edulisEp Littorina littoreaCm Chthamalus montaguiGw Enteromorpha intestinalisPd Osmundia pinnatifidaBw Fucus vesiculosusSa Sabellaria alveolataPt Gibbula umbilicalisCo Corallina officinalisCr Cladophera rupestrisUl Ulva lactuca

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

1

54

3

2

Vector extension illustrations

Perpendicular to environmental vector

Low Water

High Water

(a) Shore Position

Cm

Ul

BwPd

Gw

Sa

Cr

CoPt

MeEp

1

54

3

2

Vector extension illustrations

Perpendicular to environmental vector

(b) Stability

Axis 2

Unstable

Stable

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

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%).

26

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.

Cm

Ul

BwPd

Gw

Sa

Cr

CoPt

MeEp

Vector extension illustrations

Perpendicular to environmental vector

(a) SubmersionAxis 2

Submerged

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

Cm

Ul

BwPd

Gw

Sa

Cr

CoPt

MeEp

Vector extension illustrations

Perpendicular to environmental vector

(b) Salinity

Axis 2

Seawater salinity

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

Cm

Ul

BwPd

Gw

Sa

Cr

CoPt

MeEp

Vector extension illustrations

Perpendicular to environmental vector

(c) Turbulence / mixing

Axis 2

LessMixed by  river

Turbulent / Mixed n = 1235

Axis 1 r2 = 0.69Axis 2 r2 = 0.30

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

27

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

28

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 1 Axis 2

Rank From High Water Group Stable Group Dessicated Group Freshwater Group Mixed GroupTowards Low Water Unstable Submersed Saline Unmixed

1 M. edulis A M. edulis A M. edulis A M. edulis A M. edulis A2 C. montagui A C. montagui A G. umbilicalis A C. montagui A C. montagui A3 G. umbilicalis A G. umbilicalus A C. montagui A G. umbilicalis A L.littorea A4 L. littorea A L. littorea A L.littorea A L. littorea A G. umbilicalis A5 C. officinalis C C. officinalis C C. officinalis C C. officinalis C E. intestinalis B6 S. alveolata C S. alveolata C S. alveolata C S. alveolata C S. alveolata C7 E. intestinalis B E. intestinalis B E. intestinalis B E. intestinalis B Ulva B8 O. pinnatifida C O. pinnatifida C C. rupestris C O. pinnatifida C O. pinnatifida C9 F. vesiculosus C F. vesiculosus C F. vesiculosus C F. vesiculosus C F. vesiculocus C10 C. rupestris C C. rupestris C O. pinnatifida C C. rupestris C C. officinalis C11 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).

29

Figu

re 1

5. C

CA

com

bine

d bi

-plo

t usi

ng M

VS

P gr

oupi

ng fu

nctio

n to

illus

trate

axi

s 1

actin

g as

a v

ecto

r for

the

Myt

ilus

reef

zon

es (M

Q) t

o

the

right

, S. a

lveo

lata

(SQ

) to

the

left,

and

NQ

zon

e m

ore

even

ly

dist

ribut

ed, c

orro

bora

ting

PC

A re

solu

tion

(Fig

. 8).

68%

of v

aria

nce

extra

cted

by a

xes;

1 (5

3%);

2 (1

5%) a

nd 7

7.6%

exp

lain

ed b

y

envi

ronm

enta

l con

stra

int;

1 (6

0.5%

); 2

(17.

1%).

X= (M

Q)M

ytilus zone

= (SQ)Sab

ellaria

 zon

e

= (NQ) Zon

e to W

est

30

M. ed

ulis

S. alv

eolat

a

E. int

estina

lis

Axis 2

n = 12

35

31

To increase the strength of the environmental constraints and reduce the effect of inter-

correlation 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).

KEY

Vector extension illustrations

Perpendicular to environmental vector

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

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%).

32

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.

Distance to high water (m)(a)

S. alveo

lata occurrence de

nsity pe

r m

2

N = 58r2 = 0.16

(b)

N = 58r2 = 0.36

M. edu

lis occurrence de

nsity pe

r m2

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)).

33

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)

Distance to high water (m)

E. intestinalis  occurrence de

nsity pe

r m

2N = 58r2 = 0.05

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).

S. alveo

lata occurrence de

nsity pe

r m

2

N = 58r2 = 0.19

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)).

34

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.

N = 58r2 = 0.02

Distance to freshwater channel (m)

E. intestinalis occurrence de

nsity pe

r m

2

(a)

M. edu

lis occurrence de

nsity

 per m

2

N = 58r2 = 0.03

Distance to freshwater channel (m)(b)

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).

Distance to turbulence proxy datum (m)(a)

S. alveo

lata occurrence de

nsity pe

r m

2

N = 58r2 = 0.08

Distance to turbulence proxy datum (m)(b)

M. edulis occurren

ce den

sity per m

2

N = 58r2 = 0.08

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)).

35

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

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

Distance to turbulence proxy datum (m)

N = 58r2 = 0.01

E. intestinalis occurrence de

nsity pe

r m

2

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

SQ; Fig. 27); X26 = 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).

36

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,

show significant heterogeneity, due to the higher sand and boulder content of Zone NQ; X23

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

n= 300 n= 550n= 599*

Sand SandSand

Pebble PebblePebble

Cobble Cobble Cobble

Boulder Boulder

% Substra

te Type

Zone

Stability

* 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

no significant difference between zones; X22 = 2.359, n1 = 300 , n2 = 600, n3 = 550, P =

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

n = 300 n = 600 n = 550

Zone

Subm

ersion

 exten

t decim

al

Figure 24. Submersion extent by zone showing non – significant difference between zones

and highly skewed data with many extremes and outliers (14(a)).

37

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 value1 500 1.002 355 1.493 250 2.004 180 2.475 125 3.006 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

Larger SmallerGrain size Larger SmallerGrain size

Phi value

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

between the taxa and distinct zones was confirmed; X220 = 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

Zone NQ, was also significant (one way Chi-Square (SPSS)); X22 = 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).

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

Presen

ce  cou

nt

n = 2518

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 10 110

Zone MQ Zone NQZone SQ

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).

39

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

association; X24 = 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).

KEY:

1. E. intestinalis

2. S. alveolata

3. M. edulis

Presen

ce  cou

nt

1

1

1

2

2

2

3

3

3

n = 1078

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).

40

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 = 1450CI = 95%

HW Distan

ce to

 high water (m

)

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).

41

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

dwarf specimens populating inner Baltic habitats of salinity as low as 4-50/00 (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).

47

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).

48

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).

49

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

59

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.

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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.

61