habitat diversity relative to wave action on rocky shores: implications for the selection of marine...

AQUATIC CONSERVATION: MARINE AND FRESHWATER ECOSYSTEMS

Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 645–657 (2009)

Published online 29 December 2009 in Wiley InterScience(www.interscience.wiley.com). DOI: 10.1002/aqc.1014

Habitat diversity relative to wave action on rocky shores:implications for the selection of marine protected areas

LAURA K. BLAMEY and GEORGE M. BRANCH�

Marine Biology Research Centre, Department of Zoology, Private Bag X3, University of Cape Town,

Rondebosch 7701, South Africa

ABSTRACT

1. Current selection of marine protected areas in South Africa is based on objective criteria including biogeographicrepresentation and habitat heterogeneity. This paper specifically examines rocky shores on the west coast of SouthAfrica to determine whether they are divisible into discrete ‘habitats’ that need independent conservation.2. Seventeen rocky shores spanning the full spectrum of wave exposure were compared in terms of maximum

wave forces, biomass, species richness and diversity among zones and sites. Three biotic assemblages wereidentified, characterizing sheltered, semi-exposed to exposed, and very exposed habitats. Differences among thesewere clear-cut low on the shore but disappeared at the top of the shore where wave action was attenuated anddesiccation uniformly intense.3. The recognition of three discrete biologically-defined habitats means that rocky shores cannot be regarded as

a uniform habitat for conservation purposes. All three components need protection if the full spectrum of rocky-shore communities is to be conserved.4. It is argued that this approach allows habitats to be defined in an objective manner, and that once this has

been done, habitat heterogeneity constitutes a better measure of conservation value of an area than a ‘hotspot’approach based on species richness and endemism.Copyright r 2008 John Wiley & Sons, Ltd.

Received 16 May 2008; Revised 26 August 2008; Accepted 10 September 2008

KEY WORDS: intertidal biomass; limpets; mussels; marine protected areas; rocky shores; wave action

INTRODUCTION

Selection of sites for conservation has become increasingly

sophisticated with development of algorithms to compare the

rival merits of different sites (Margules and Pressey, 2000;

Leslie et al., 2003). However, the underlying philosophy

remains contentious, with two approaches— ‘hotspots’

versus ‘habitats’—being debated. The ultimate aim of both

is protection of biodiversity, but in different ways. Species are

the biological unit for hotspot designation, with preference

given to conservation of areas high in species richness and

endemism (Myers et al., 2000). By contrast, the ‘habitat’

approach seeks to protect all habitats within all biogeographic

regions, and favour areas that include a high diversity of

habitats (Hockey and Branch, 1997; Roberts et al., 2003a,b).

For most of the 20th century, terrestrial ecosystems have

received the greatest attention in terms of conservation

(Roberts et al., 2003a), so it is no surprise that the design of

reserves has been developed primarily for terrestrial

ecosystems. However, fundamental differences exist between

marine and terrestrial ecosystems, such as geographic

boundaries, dimensions of species distribution and transport

of energy, materials and organisms, implying some unique

criteria in the selection of marine reserves (Hockey and

Branch, 1994; Carr et al., 2003; although Avery et al., 2003

offers a counter view).

Early attempts at developing criteria for Marine Protected

Areas (MPAs) were focused largely on biological measures

such as rarity and diversity (Hockey and Branch, 1994).

Lately, there has been a shift towards incorporating political,

social and economic criteria owing to increasing recognition

that MPAs should fulfil multiple objectives (Kelleher and

Kenchington, 1990; Hockey and Branch, 1997; Friedlander

et al., 2003). Several schemes have been proposed to help select

*Correspondence to: George M. Branch, Marine Biology Research Centre, Department of Zoology, Private Bag X3, University of Cape Town,Rondebosch 770, South Africa. E-mail: [email protected]

Copyright r 2008 John Wiley & Sons, Ltd.

sites for MPAs. Hockey and Branch (1997) developed a

methodology called COMPARE (Criteria and Objectives for

Marine Protected ARea Evaluation) that rates rival sites in

terms of their relative value for conservation. This is based on

a set of 14 objectives for MPAs, covering biodiversity

protection, fisheries management and human utilization, and

a set of 17 criteria that rate the degree to which given areas

meet these objectives. Roberts et al. (2003a,b) subsequently

developed more detailed ecological criteria to evaluate

candidate sites.

Hockey and Branch (1994, 1997) advocate that as a first

step biogeographic regions need to be defined and

conservation implemented in all such regions. They further

argue that nested within each biogeographic region, habitat

heterogeneity should be used to measure the relative prestige of

different areas rather than species richness, because different

habitats are fundamentally different in species richness, and

should not be accorded greater (or lesser) value simply because

they have a high (or low) species richness (Roberts et al.,

2003a). Rather, protected areas should include as many

habitats as possible since the diversity of biological

communities present is related to the range of habitats

present (Hockey and Branch, 1997). Using habitat

heterogeneity as a proxy for biodiversity has been

successfully applied to the initial selection of MPAs (Ward

et al., 1999), and has advantages over the species-based

hotspot approach, as discussed below, but it does require that

habitats be defined. Various terms have been used to describe

these units, including ‘land classes’ (Lombard et al., 2003) or

‘land systems’ (Oliver et al., 2004) and ‘broad habitat units’

(Cowling and Heijnis, 2001). Beger et al. (2003) use the word

‘eco-habitats’ and define them as areas with ‘a distinct

community structure owing to different environmental

factors’. Here, the word ‘habitats’ is used in the same sense,

and it is argued that they must initially be distinguished by the

biota present, although they may ultimately be recognized by

surrogates including physical conditions (e.g. substrate type

and wave action) and biological features such as the dominant

characteristic morphotypes (e.g. mangroves). At a coarse level,

nearshore marine habitats can be defined simply by

distinguishing obvious systems such as mangroves, salt

marshes, sandy beaches, coral reefs or rocky shores.

However, each of these may be divisible into discrete types,

often with radically different biotic communities. Banks and

Skilleter (2007) showed that when applying these coarse level

surrogate measures for biodiversity, they failed to represent

fine-scale habitat and microhabitat diversity and were

therefore less likely to achieve conservation goals. Securing

conservation of each of these smaller-scale units is thus

necessary to protect all community types and maintain all

ecosystem functions. This necessitates defining the units in an

objective and defensible manner.

This paper makes a specific contribution to the habitat

approach by inquiring about the extent to which rocky shores

in the cool-temperate Namaqua Province of southern Africa

are divisible into ‘habitats’ (operationally recognized by their

discrete assemblages of species) that can be objectively

distinguished by quantitative means.

Rocky shores display distinct zonation patterns vertically

and horizontally along shores. Locally, the two most

important physical forces responsible for these gradients are

probably desiccation and wave action, respectively (Dayton,

1971; Sousa, 1979; Menge and Sutherland, 1987; Bustamante

et al., 1997).

Clearly there is need to quantify intertidal community

structure of rocky shores relative to wave action to objectively

determine whether rocky shores are just a single variable

habitat, or comprise different habitats with distinctive

biological communities, each needing conservation. A

substantial amount of research has been done on the effects

of wave exposure on various intertidal organisms/communities

(Ballantine, 1961; Lewis, 1964; Denny, 1985, 1987; Denny

et al., 1985; McGuinness, 1987a,b; Brown and Quinn, 1988;

Carrington, 1990; Bertness et al., 1991; Gaylord et al., 1994;

Bustamante and Branch, 1996a; Blanchette, 1997; Andrew and

Viejo, 1998; Jenkins and Hartnoll, 2001; Harley and Helmuth,

2003; Davenport and Davenport, 2005), but most of these

studies have either focused on the biomechanical implications

of wave action for particular taxa, or have descriptively

distinguished community composition at different grades of

wave action. In South Africa previous studies on rocky-shore

community structure relative to wave action have tended to

contrast subjectively defined ‘exposed’ and ‘sheltered’ sites,

often without quantification of wave action (McQuaid and

Branch, 1984; Bustamante and Branch, 1996a; Bustamante

et al., 1997; but see Steffani and Branch, 2003b,c). The current

study is an advance because it covered the full spectrum of

wave exposure, quantitatively measured both wave action and

biotic composition, and sought objective measures of

community distinctiveness.

Two hypotheses were advanced as a framework. First,

differences in community composition were expected to be

clear-cut in the low shore but non-existent in the high-shore as

the effects of wave action are attenuated upshore and

superseded by the uniformly severe desiccation. Second,

considering entire shores, it was expected that the intensity

of wave action would determine community structure and thus

separate shores with different levels of wave exposure.

METHODS

Study site

The study was located roughly 600 km north of Cape Town on

the west coast of South Africa in a relatively pristine stretch of

coastline around Groenrivier. Its pristine condition, high

diversity of habitats and representation of the Namaqua

biogeographic province (Emanuel et al., 1992) were prime

motivations for a ministerial declaration of intention in

February 2004 to promulgate the area as an MPA.

Samples were taken during the autumn/winter at 17 sites

spanning the full spectrum of wave exposure (Figure 1).

Maximum wave force was measured at 14 of these sites using a

maximum wave-force dynamometer (Steffani and Branch,

2003b), and estimates interpolated for the three additional

sites. Wave forces were divided a priori into four categories:

sheltered, semi-exposed, exposed, and very exposed,

respectively experiencing maximum wave forces of 0–5, 5–10,

10–15 and 415 (� 103) Nm�2, following Steffani and Branch

(2003a).

L.K. BLAMEY AND G.M. BRANCH646

Copyright r 2008 John Wiley & Sons, Ltd. Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 645–657 (2009)

DOI: 10.1002/aqc

Sampling methods

At each site, five replicate transects from the low shore to

the high shore were randomly chosen. Within each transect,

four 0.25m2 quadrats (divided into 100 squares each

representing 1% cover) were sampled in the low, mid, high

and top zones.

Percentage cover of sessile fauna and flora and numbers of

mobile organisms were scored (Bustamante and Branch,

1996a). Subsamples of species were taken to convert

abundance to biomass (g wet weight m�2).

Statistical analyses

The biomass data were analysed with PRIMER (Plymouth

Routines In Multivariate Ecological Research version 5.2.2).

The data set was too large to simultaneously analyse all

replicates (n5 340 samples) so two complimentary analyses

were undertaken. First, the biomass values for each species

were averaged for the five replicates per zone, thus reducing

the replicates per site from 20 to 4, yielding 68 samples. This

produced a ‘site-by-zone’ comparison of all zones among sites

(Figure 2). Second, the data for each species were averaged for

Figure 1. Map showing location and detail of sites sampled at Groenrivier (1) and Cape Columbine and Jacobsbaai (2). Site codes, coordinates andmaximum wave force measurements per site are listed. Estimates of wave force were made for sites at Cape Columbine and Jacobsbaai.

HABITAT DIVERSITY RELATIVE TO WAVE ACTION ON ROCKY SHORES 647


DOI: 10.1002/aqc

the four zones in each replicate transect to give single values

for each transect (distilling the data to five replicate transects

per site; Figure 2). These data allowed a ‘site-by-transect’

analysis comparing all replicate transects among sites. Both

data sets were analysed by Bray–Curtis hierarchical clustering

and multidimensional scaling (MDS) to compare community

structure among sites and zones. SIMPER (similarity

percentage) analysis (with a cut off of 85%) was performed

on the ‘site-by-transect’ data to determine which species were

responsible for similarities or dissimilarities among and within

sites.

The zones and sites were also compared in terms of species

richness, species diversity and biomass. Species richness was

calculated by counting the number of different species present

per zone (pooling the five replicates). Species diversity was

measured using the Shannon–Wiener function (H05�Sipi(log2 pi)), based on biomass values. Species richness, diversity,

overall biomass, and filter-feeder, grazer and algal biomass

were regressed against wave force at each site, using

polynomial regressions.

Validation

Once discrete habitats based on the biota had been defined the

classification was independently validated. First, using aerial

orthophotographs showing coastal contours (scale 1:10 000)

and considering prevailing wave swell direction, five rocky-

shore sites lying in the same biogeographic province but

250 km to the south of Groenrivier were selected: sites D, I, S

and V representing sheltered, semi-exposed, exposed, and very

exposed situations at Cape Columbine, and Site E representing

a sheltered site at Jacobsbaai (Figure 1). These sites were

assigned to the habitat types as defined, based on the wave

exposure that was anticipated on the basis of coastline

topography and wave direction. Second, the biota at each of

these sites was sampled as above (during autumn/winter) and

then these data were inserted with those obtained from

Groenrivier and the analysis rerun with Primer to test

whether the additional sites were indeed classed in the

habitat types it was assumed they would represent. This

provided two tests of the approach: (1) whether other localities

outside the initial study area conformed to the proposed

classification of habitats, and (2) whether a simple physical

surrogate (estimated wave action) could serve as a proxy for

the biologically-defined habitats.

RESULTS

Zonation

Comparing among zones, both cluster analysis (Figure 3(a))

and MDS (Figure 3(b)) distinguished six groups. Group 1

united the top zones for all sites, spanning all grades of wave

exposures (C–V). Group 2 was made up of the upper-most

zones (i.e. the high and top zones) for mainly sheltered sites

(Q3 being an exception as it was at an exposed site). Group 3

comprised a mixture of mid and high zones from exposed or

very exposed sites. Group 4 contained the low and mid zones

from very exposed sites T–V. Group 5 also incorporated the

low and mid zones, but from semi-exposed to exposed sites

(mostly G–S). Group 6 consisted of low zone and mid zone

samples from the six sheltered sites A–F.

Wave exposure

Pooling across zones to compare the five replicate transects at

each site, both the hierarchical cluster analysis (Figure 4(a))

and the MDS plot (Figure 4(b)) revealed five clusters. Group 1

comprised the very exposed sites T–V. Two exposed sites, K

and Q made up group 2, and the exposed sites L, M, N, O, P

and R constituted group 3. Group 4 included three semi-

exposed sites, G, H and I, to which a fourth site, J, was

added because (a) it was allied with the other semi-exposed

sites in the MDS if not in the cluster analysis, and (b)

measurements of wave action there classed it as semi-exposed.

Group 5 comprised sites A–F, which were all sheltered.

Typically, the replicate transects at each site clustered closely

and were more similar to each other than to transects at other

sites. Sheltered and very exposed sites formed completely

Top

High

Mid

Low

Sea

1 2 3 4 5

Mean zone(n = 4 zones)

Mean transect(n = 5 transects)

Rocky shorezones

TransectsMHWS

MLWS

Figure 2. Sampling protocol and pooling of data: at each site four zones were sampled in five replicate transects. For analyses, the quadrat data ateach site were pooled horizontally to create mean low, mid, high and top zones, and vertically to create five replicate transects.



DOI: 10.1002/aqc

discrete clusters, but those classed as semi-exposed and

exposed tended to merge.

Validation of patterns

Five sites from outside the study area (D, E, I, S and V) were

first rated according to their estimated wave action and then

sampled to examine their community composition. All five

yielded community compositions that classed them with other

sites of comparable wave action (Figure 4).

Characteristic species

Species characterizing the four grades of wave action

recognized a priori were identified by SIMPER (Figure 5).

Sheltered shores were typified by the limpets Cymbula

granatina and Scutellastra granularis, which contributed

52.4% and 12.1%, respectively, to this similarity. Semi-

exposed shores were characterized by the mussel Mytilus

galloprovincialis and the limpet Scutellastra argenvillei. On

exposed shores M. galloprovincialis and S. argenvillei were

100

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Mid-highExposed & very exposed

Low-midVery exposed

Low-midSemi-exposed & exposed

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Stress:0.121. Top zones

All grades of wave action

2. High-top Sheltered

3. Mid-high Exposed & Very exp

6. Low-mid Sheltered

4. Low-mid Very exp

5. Low-mid Semi-exp & Exposed

(a)

(b)

Figure 3. (a) Dendrogram showing hierarchical cluster analysis and (b) MDS plot based on standardized root transformed biomass data from four zones(1–4, where 15 low zone, 25mid zone, 35high zone and 45 top zone) at 22 sites (A–V). The data for each zone were an average of five replicates.



DOI: 10.1002/aqc

again the major contributors but in different proportions. Very

exposed shores were characterized by M. galloprovincialis and

Plocamium cornutum.

Dissimilarities among shores with different grades of wave

action were defined by distinguishing species (Figure 5).

Sheltered shores were 75.3% dissimilar to semi-exposed

shores. The components most responsible were Cymbula

granatina, the algae Porphyra capensis and Ulva species, and

the whelk Burnupena lagenaria (all with biomasses that were

greatest on sheltered shores and declined progressively with

wave action); and Mytilus galloprovincialis, Scutellastra

argenvillei and the alga Champia lumbricalis (absent or scarce

on sheltered shores). Semi-exposed and exposed shores were

45.17% dissimilar due to S. argenvillei, C. lumbricalis,

C. granatina and S. granularis being more abundant on semi-

exposed shores and M. galloprovincialis, Scutellastra cochlear

and G. capensis reaching higher values on exposed shores

(Figure 5). Finally, the average dissimilarity between the

exposed and very exposed shores was 46.38%, largely due to

Plocamium cornutum, Corallina species and S. cochlear (greater

biomasses on very exposed shores) and S. argenvillei,

M. galloprovincialis and G. capensis (lower values there).

Diversity and biomass

Species richness peaked at semi-exposed to exposed shores

(Figure 6(a)). There was, however, no significant trend in

species diversity (Figure 6(b)). Total biomass also peaked at

intermediate intensities of wave action (Figure 6(c)). Not

surprisingly, filter feeders (which constituted the bulk of the

biomass) followed the same trend (Figure 6(d)). One site (N)

had an exceptionally high biomass of filter feeders (35 kgm�2),

inflating overall biomass as well; but even if this site was

excluded from the analysis, the polynomial relationship

100

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23 4 5

6

T U UV V K Q N P L LO RM MS I IH H J G F C E A B D

ShelteredSemi-exposedExposedExposedVery exposed

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Stress: 0.1

6. Sheltered

4.Semi exposed

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1. VeryExposed

3. Exposed

(a)

(b)

5.Semi exposed

Bra

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is S

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Figure 4. (a) Dendrogram showing hierarchical cluster analysis and (b) MDS plot based on standardized root transformed biomass data from 22sites (A–V) with five replicate transects per site.



DOI: 10.1002/aqc

remained significant and biomass still peaked at intermediate

wave forces. Grazer biomass was high at sheltered and semi-

exposed sites, predominantly due to Cymbula granatina and

Scutellastra argenvillei respectively, but declined at high

intensities (Figure 6(e)). There was no significant relationship

between algal biomass and wave force, despite a tendency to

increase at higher exposures (Figure 6(f)). Predator/scavenger

biomass was too low to consider statistically.

DISCUSSION

What is a habitat?

To employ habitats as a surrogate for biodiversity surveys

requires that they first be defined. As Stevens and Connolly

(2004, p. 351) state: ‘Little confidence can be placed in marine

habitat classifications based solely or largely on abiotic

surrogates without calibration by rigorous biological

surveys’. This survey addressed this question with a specific

focus on rocky shores on the west coast of South Africa, but

the approach is applicable to other regions and other

ecosystems.

The term ‘habitats’ is used to distinguish spatial units that

have biological assemblages that can be distinguished by

objective quantitative means. In doing so, it is recognized that

such units are a response to an interplay between physical

conditions and biotic interactions, and that their ultimate

definition must incorporate the physical factors that set the

scene.

To be useful, habitats must be objectively definable, and

easily distinguishable.

Defining rocky-shore habitats

Zonation

The differences recorded between zones supported our first

hypothesis that greater diversity and heterogeneity exist in the

low shore: divergence between sites was greatest in the

lowshore and diminished upshore. This accords with the idea

that desiccation-related factors constitute a uniform stress at

the top of the shore, whereas wave action is not uniform in the

low shore (Bustamante et al., 1997). This is, however, largely

of academic interest when choosing MPAs, because entire

rocky shores will be selected for protection, not individual

zones within sites, although it is true that in principle MPAs

are often zoned for different activities. Thus comparisons of

the differences among sites were the issue of central interest

when deciding how many discrete habitats should be

recognized on rocky shores.

Wave action

Corresponding to the wave-force measurements, biotic clusters

were evident in the analysis of community composition

(Figure 4), supporting the second hypothesis that intensity of

wave action influences community structure, as noted by many

authors (Ballantine, 1961; Lewis, 1964; Dayton, 1971;

McQuaid and Branch, 1984; Denny et al., 1985; Emanuel et

al., 1992; Bustamante et al., 1995a,b, 1997; Bustamante and

Branch, 1996a; Steffani and Branch, 2003a,b; Branch and

Steffani, 2004). The gradient evident in Figure 4 (extending

from the bottom left corner up to the top right corner) displays

groupings from the most sheltered sites (group 5) moving

through a cluster of semi-exposed and exposed sites (groups 2,

3 and 4) and finally, a cluster of the very exposed sites

(group 1). Groups formed between sites that experienced

similar levels of wave exposure.

Biological characterization

Three readily distinguishable clusters emerged: sheltered, semi-

exposed plus exposed, and very exposed, corresponding to

wave forces of o5, 5–15 and 415 (� 103) Nm�2. Greater

distinction between semi-exposed and exposed shores might

have been expected, but they merged. Previous work in the

Plocamium cornutum Scutellastra cochlearCorallina spp.Mytilus galloprovincialisLaminaria pallidaGunnarea capensisScutellastra argenvilleiScutellastra granularisChampia lumbricalisPorphyra capensisUlva sp.

Cymbula granatina

Sheltered(S=56.2%)

Biomass (log g/m2)

***

Semi-exposed(S=61.6%)

Exposed(S=64.2%)

Very exposed(S=72.5%)

***

*

-1 0 1 2 3 4 -1 0 1 2 3 4 -1 0 1 2 3-1 0 1 2 3 4

**

*

**

***

***

*

*

*

*

D=75.3% D=45.2% D=46.3%

Burnupena lagenaria

00

0

0

000

0

Characteristic species

Distinguishing species

Figure 5. Average wet biomass (log gm�2) of species responsible for the similarities (S) within grades of wave action and the dissimilarities (D)between sheltered and semi-exposed shores, semi-exposed and exposed shores, and exposed and very exposed shores. Significance differences areshown for �0.05, ��0.01 and ��0.001 levels. White dots identify species distinguishing between grades of wave action; black dots identify species

characteristic of each level; 05 absence.



DOI: 10.1002/aqc

region has shown that the alien mussel Mytilus

galloprovincialis dominates exposed shores whereas the

limpet Scutellastra argenvillei achieves exceptionally high

densities on semi-exposed shores and retards the

development of mussel beds (Steffani and Branch, 2003a,b,c;

Branch and Steffani, 2004). This was reflected in the results, as

M. galloprovincialis attained highest biomasses on exposed

shores whereas S. argenvillei was more abundant on semi-

exposed shores (Figure 5). Despite these and comparable

significant differences for other species, the aggregate effect

was too blurred to consider semi-exposed and exposed shores

as separate habitats. The dissimilarity between semi-exposed

and exposed shores was smaller than for any other

comparisons among grades of wave action. Moreover, little

value was placed on M. galloprovincialis as an indicator

because it is an alien species. Thus, it was considered that only

three distinct types of habitat exist on the rocky shores

examined, all of which require conservation.

Indicator species

The similarities within the four grades of wave action

distinguished a priori, and the dissimilarities between them,

were due to a few select species. Sheltered shores were

dominated by the limpet Cymbula granatina, which is known

to be intolerant of wave-exposed conditions because of its low

tenacity (Branch and Marsh, 1978), as is its sister species

C. oculus (Branch and Odendaal, 2003). C. granatina was the

2y= -0.023x + 0.317x + 1.7712r = 0.201; p<0.05

35

45

55

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2 4 6 8 10 12 14 16 18 200


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2 4 6 8 10 12 14 16 18 200


2 4 6 8 10 12 14 16 18 200


Categories of Wave Force (x10 3N.m-2)

0-5 5-10 10-15 >15

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2y = -0.160x + 3.526x - 6.4512r = 0.258; p < 0.02

2y= -0.289x + 5.751x + 26.1172r = 0.298; p < 0.01

2y= -0.143x + 3.223x - 9.0082r = 0.243; p = 0.02

Figure 6. (a) Species richness, (b) Shannon–Wiener diversity, (c) overall biomass, (d) filter-feeder biomass, (e) grazer biomass and (f) algal biomass,all relative to maximum wave forces experienced at the 22 sites sampled (A–V). Curves are polynomial trend lines.



DOI: 10.1002/aqc

species most responsible for the similarity between the four

sheltered sites, and their dissimilarity to semi-exposed sites

(Figure 5). At all shores with wave forces greater than

‘sheltered’, M. galloprovincialis and the limpet S. argenvillei

were abundant. S. argenvillei was most abundant on semi-

exposed shores, M. galloprovincialis on exposed shores, and

both diminished on very exposed shores (see also Branch and

Steffani, 2004). Other species helped distinguish these

communities: the red alga Champia lumbricalis peaked on

semi-exposed shores, the tubeworm Gunnarea capensis on

exposed shores, and the red algae Plocamium cornutum and

Corallina spp. on very exposed shores.

Communities on shores with different grades of wave

action were also functionally different. On sheltered shores, C.

granatina controls algal growth but depends on a subsidy of

drift kelp from subtidal kelp beds to sustain its high densities

(Bustamante et al., 1995b). Only in the high shore did the

influence of grazers abate, and there hardy algae such as

Porphyra capensis and Ulva spp. proliferated.

On semi-exposed and exposed shores, S. argenvillei was

abundant, curtailing foliar algae but promoting encrusting

corallines. It too depends on access to fronds of subtidal kelp

plants to maintain its populations (Bustamante et al., 1995b).

Dominant filter-feeders, notably M. galloprovincialis and G.

capensis, are also maintained by particulate matter derived

from the kelp beds (Bustamante and Branch, 1996b). Shores

spanning sheltered to exposed therefore depend on various

forms of subsidization from subtidal kelp beds and are net

importers of materials (Hartnoll, 1983). Provisioning of

intertidal shores on the west coast of South Africa by

products from subtidal kelp beds is central to the high

overall biomass recorded there (Bustamante and Branch,

1996a).

On very exposed shores, almost all species diminished and

the system appeared to be physically rather than biologically

controlled. Plocamium cornutum and upright corallines peaked

there, promoted by their tolerance of wave action, diminished

spatial competition from mussels, and reduced grazing by

limpets.

Overall patterns relative to wave action

Species richness peaked at intermediate levels of wave action

(Figure 6(a)), as might be predicted by the intermediate

disturbance hypothesis, with biological control by a few

dominant species at the lower end of the wave action

spectrum, and physical control of most species at the upper

end (Connell, 1978). Shannon–Wiener diversity showed no

relationship with wave action (Figure 6(b)), probably due to

dominance by a few species: other work has shown that

diversity indices are sensitive to dominance (Soetaert and

Heip, 1990; Gray, 2000). Where communities are dominated

by a single species, diversity is reduced relative to sites with a

more even spread of species (McQuaid and Branch, 1984). The

greater richness of species at intermediate levels of wave action

was therefore offset by a reduction in evenness, levelling

diversity across the gradient of wave action.

Total biomass was clearly domed, being maximal at

intermediate wave intensity (Figure 6(c)), predominantly

influenced by filter feeders, which dominated the biomass

and exhibited the same pattern (Figure 6(d)). McQuaid and

Branch (1984), McQuaid et al. (1985) and Bustamante and

Branch (1996a) have previously described how filter-feeder

biomass is greater on wave-exposed than sheltered shores, but

they sampled only two grades of wave action. When Steffani

and Branch (2003a,b,c) sampled mussels across the entire

spectrum of wave action intensities experienced on the west

coast, domed relationships emerged for biomass, growth,

recruitment and condition (Branch and Steffani, 2004). The

shape of the biomass curve for filter feeders reflects inadequate

supplies of food at the sheltered extreme of the spectrum, and

physical limitations at the very exposed extreme. The major

contributor to filter-feeder biomass, M. galloprovincialis,

interacts strongly with grazers, depressing the biomass of the

S. argenvillei at sites where wave action is strong, but

coexisting with it at semi-exposed sites (Branch and Steffani,

2004). This uneasy truce between the two species was the main

factor distinguishing between semi-exposed and exposed

shores: M. galloprovincialis peaked on exposed shores and S.

argenvillei on semi-exposed shores. A notable feature of all

grades of wave action was the scarcity of barnacles. It is likely

that upwelling—a feature of the region—exports barnacle

larvae and provides little opportunity for their return to the

shore. In California and Chile barnacle settlement is inversely

related to upwelling (Connolly et al., 2001; Navarrete et al.,

2005). Maximum values of filter-feeder biomass as a whole

were higher than those for sites with equivalent wave action on

the south and east coasts of South Africa (Van Erkom

Schurink and Griffiths, 1990; Bustamante et al., 1995a;

Bustamante and Branch, 1996a), reflecting the higher

productivity on the west coast associated with upwelling.

Grazer biomass was high at most sites, but declined where

wave action was strong to very strong (Figure 6(e)), probably

curtailed by the physical demands of remaining attached and

inhibition of feeding (Denny et al., 1985). High biomasses at

sheltered sites were attributable to C. granatina, and those at

semi-exposed sites to S. argenvillei, both of which reach

exceptionally high densities and biomasses on the west coast of

South Africa, supported by subsidies of drift or attached kelp

provided by subtidal kelp beds (Bustamante et al., 1995b).

Algal biomass was generally low, implying that all shores

would have been net importers of energy. With one exception,

grazer biomass always exceeded algal biomass, emphasizing

the importance of subsidies to maintain primary consumers.

As recorded elsewhere in South Africa (McQuaid et al., 1985),

algal biomass tended to increase with exposure, as the biomass

of grazers declined (Figure 6(f)), so that reliance on imported

materials increases as wave action declines—contrary to

Hartnoll’s (1983) observation that sheltered shores are

normally net exporters and exposed shores net importers.

Test-driving the process

For the classification of rocky shores into three habitats to be

useful, it needs to be applicable elsewhere. A previous analysis

by Emanuel et al. (1992) indicates this is the case, at least

within the limits of the Namaqua biogeographic province.

Across the 1200 km Namaqua coast, they demonstrated that

sites with comparable wave action had greater similarities in

community composition than were evident between sites that

were closely situated but experienced different intensities of

wave action. One can therefore be confident that wave action

does act as a useful means of identifying distinct habitats with

quantifiably different communities.



DOI: 10.1002/aqc

These conclusions were verified by sampling five sites

250 km to the south of the study area but still within the same

biogeographic region. The classification of all five concurred

with the division of habitats into three levels of wave action.

This increased confidence both in the classification and in

the ability to estimated wave intensity from simple measures

of topography and prevailing wave direction. This has

important implications. It suggests that not only can

community composition be estimated from wave intensity,

but that the latter can be estimated from easily obtainable

indirect measures. If it is possible to estimate wave forces

consistently from topographic maps or orthophotographs and

data on prevailing wind and wave directions, then entire

regions can be mapped from remote data to determine the

position and extent of rocky-shore habitats.

It is still necessary to fully test the validity of such physical

surrogates, and to do so across biogeographic boundaries, to

explore their generality.

Application

The first level of decision-making in the hierarchy advocated

by Hockey and Branch (1997) and Roberts et al. (2003a,b) is

that conservation be accorded to all biogeographic provinces.

The first steps of determining biogeographic patterns around

the coast of southern Africa were taken early (Stephenson,

1939; summarized in Stephenson and Stephenson, 1972), and

since refined by Emanuel et al. (1992) and Sink et al. (2005).

Five distinct biogeographic provinces are now recognized.

These are well defined for the intertidal and shallow subtidal

zones, but with increasingly less precision as one moves

offshore, where information on the distribution of species is

deficient. Lombard et al. (2004) provisionally dealt with this by

recognizing ‘bioregions’ that incorporate both along-shore

biogeographic patterns and likely changes in community

composition that will occur as one moves offshore, and

employed these bioregions when recommending possible

locations of future MPAs.

Within bioregions, all habitats need protection. This is the

second tier in the hierarchy of conservation goals. Based on the

current data, at a minimum, rocky-shore habitats with three

grades of wave action are sufficiently distinct to require

independent protection: sheltered, semi-exposed to exposed,

and very exposed. Although quantitatively distinguishable in

terms of their maximum wave forces (0–5, 5–15 and

415� 103Nm�2), they could also be recognized from simple

estimates based on topography and wave orientation. The

three habitats also constitute functionally different entities: (i)

sheltered shores dominated by Cymbula granatina and

dependent on drift kelp; (ii) moderately exposed shores

dominated by Scutellastra argenvillei and Mytilus

galloprovincialis, dependent on attached kelp and particulate

fragments; (iii) very exposed shores that are physically-driven

and where algae such as Plocamium cornutum and Corallina

spp. reach their primacy.

In this context, a comparison with sandy beaches is

instructive. Sandy beaches can be classified on a continuum

from reflective (steep, coarse-grained, with oncoming waves

directed at the face of the beach) to dissipative (shallow, fine-

grained, with strong offshore wave action that is dissipated

over the flat beach). Species richness rises from reflective to

dissipative conditions (McLachlan et al., 1993). Importantly,

this involves the addition of species, not the substitution of one

set of species by another. Thus, reflective beaches contain a

subset of the species found on dissipative beaches, and

conservation of representative dissipative beaches will protect

more species and will cover all those species found on reflective

beaches. Dissipative beaches therefore assume priority over

reflective ones in terms of conservation value. This situation

contrasts with that on rocky shores, where different suites of

species are involved at different intensities of wave action, and

protection of one type of habitat would not offer protection to

assemblages in other habitats.

A preliminary analysis of conservation on the west coast of

South Africa has already been undertaken, revealing that (1)

there is inadequate protection granted to the Namaqua

biogeographic province; (2) the coast is subject to intense

fishing and diamond-mining that conflict with conservation

goals; and (3) the region between the Groen and Spoeg Rivers

that constituted the study area contains a high diversity of

habitats and is a priority area for future conservation

(Emanuel et al., 1992).

Recognition of the role of MPAs

‘No-take’ Marine Protected Areas (MPAs) are now well

established as a ‘necessary but not sufficient’ tool for marine

conservation (Allison et al., 1998) and have repeatedly been

proven to increase average size, density and biomass of

individual species and the diversity of communities (Bennett

and Attwood, 1991; Polunin and Roberts, 1993; Allison et al.,

1998; Halpern, 2003). However, to reach their full potential,

MPAs must be selected on sound ecological criteria that

maximize their planned goals (Hockey and Branch, 1994;

1997; Allison et al., 1998; Carr et al., 2003; Halpern, 2003;

Roberts et al., 2003a,b). But how should we best select and

design MPAs?

Rival merits of different approaches to identifying

conservation priorities

There are two distinct aspects to the challenge of selecting

MPAs. The first is whether species or habitats serve as the

most useful units for decisions. Actions based on species do

ensure that all species are considered, but usually ignore the

relative abundance of species and require detailed information

that seldom exists for all taxa. Attempts have been made to test

whether higher taxa can be employed instead of species, or if

well-known taxonomic groups can be used as a surrogate for

all species. The results have been mixed. Gladstone (2002)

showed that molluscs are an effective indicator group but

macroalgae are not; and Beger et al. (2003) argued that groups

that are spatially heterogeneous such as fish are better

indicators than those that are not, such as corals. By

comparison with analyses based on species, habitats are

easier to map, require less demanding information, and are

better at incorporating the structure and functioning of

ecosystems and communities (Sladek Nowlis and

Friedlander, 2004).

The second issue is the rival merits of ‘hotspot’,

biogeographic and complementarity approaches. The

‘hotspot’ approach focuses on species richness and endemism

(Myers, 1990; Myers et al., 2000), favouring conservation of

regions with high levels of richness and endemism. The



DOI: 10.1002/aqc

underlying philosophy is that such areas yield maximal gains

by protecting the greatest number of species and/or endemics

relative to investment, a strategy adopted by Conservation

International (Mittermeier, 2000). The approach is at its best

in identifying entire regions most worthy of conservation, and

in rating comparable systems, as Roberts et al. (2002) have

done in identifying conservation priorities for tropical reefs.

But even then, Lourie and Vincent (2004) argue that it is only

applicable within biogeographic regions. It certainly cannot

usefully compare different types of ecosystems or habitats even

within a region—say sandy beaches (which have low diversity)

with tropical coral reefs (with high diversity)—because there is

no intrinsic reason why high-diversity ecosystems should

warrant greater conservation than low-diversity ones. For

this reason, the hotspot approach risks overlooking low-

diversity habitats, areas or regions.

Hotspot identification requires detailed information on

species distribution. In contrast, once habitats are defined, they

can be rapidly mapped with minimal expertise. They are also

amenable to identification with remote sensing or via simple

proxies such coastal topography and prevailing winds.

The biogeographic approach dictates that selection of

conservation areas must first be based on identification and

coverage of all biogeographic regions, and that within each

biogeographic region, adequate protection be afforded to all

habitats (Hockey and Branch, 1994; Roberts et al., 2003a,b).

In its infancy, this approach advocated selection of protected

areas at the edges and centres of biogeographic regions

(Hockey and Branch, 1997), but with development of

algorithms based on iterative, optimizing or simulated

annealing approaches, it is now possible to identify the most

efficient complementary combinations of areas within

biogeographic regions that will provide desired levels of

protection for habitats or species (Leslie et al., 2003).

Pressey and Nicholls (1989), Turpie et al. (2000), Awad

et al. (2002), Beger et al. (2003), and Fox and Beckley

(2005) have compared hotspots, biogeographic and comple-

mentarity approaches in terms of their efficiency in achieving

representative conservation. Biogeographic and comple-

mentarity approaches had fairly similar efficiencies. Using

hotspots proved least efficient because attention falls on sites

with high species richness or endemism, which are often closely

situated and therefore contain similar suites of species.

Moreover, species richness hotspots often failed to coincide

with endemism hotspots, so that species-rich localities did not

protect many endemics and vice versa.

Thus it is argued, first, that using habitats rather than (or in

addition to) species as the units of conservation is superior in

several respects, and essential when detailed data on species

distributions are lacking. Second, it is necessary to combine

biogeographic and complementarity analyses of habitats (or

species) in a hierarchical manner, with biogeographic analyses

defining the first step, and complementarity analyses of

habitats taking place within each biogeographical region to

efficiently preserve all habitats (Hockey and Branch, 1997;

Roberts et al., 2003a,b).

In conclusion, rocky shores on the west coast of South

Africa constitute at least three discrete habitats. Failure to

protect all three habitats would mean that the full spectrum of

community types occupying rocky shores would not gain

protection. It would have been inappropriate to apply the

‘hotspot’ concept to the data to decide conservation priorities

among habitats: highest diversity would have favoured

protection of shores with intermediate levels of wave action,

ignoring the fact that different suites of species and different

functional attributes exist for each habitat. It is believed that

habitat diversity is a better measure of the conservation value

of an area than can be derived from a consideration of species

richness and endemism.

ACKNOWLEDGEMENTS

We thank Nina Steffani, Tammy Robinson, Paula de Coito,

Matthew Bird, and Evie Wieters for help in the field, and

Hedley Grantham and an anonymous reviewer for their

helpful comments. Funding was received from the University

of Cape Town, the National Research Foundation and the

Andrew Mellon Foundation.

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DOI: 10.1002/aqc

habitat diversity relative to wave action on rocky shores: implications for the selection of marine...

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