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
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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,
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).
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