a rapid biodiversity assessment methodology tested on intertidal rocky shores

AQUATIC CONSERVATION: MARINE AND FRESHWATER ECOSYSTEMS

Aquatic Conserv: Mar. Freshw. Ecosyst. 20: 452–463 (2010)

Published online 30 March 2010 in Wiley InterScience(www.interscience.wiley.com). DOI: 10.1002/aqc.1111

A rapid biodiversity assessment methodology tested on intertidalrocky shores

TIMOTHY D. O’HARAa,�, PRUE F. E. ADDISONa, RUTH GAZZARDa, TRUDY L. COSTAb

and JACQUELINE B. POCKLINGTONb

aMuseum Victoria, GPO Box 666, Melbourne 3001, AustraliabZoology Department, University of Melbourne, Melbourne 3010, Australia

ABSTRACT

1. Conservation managers require biodiversity assessment tools to estimate the impact of human activities onbiodiversity and to prioritize resources for habitat protection or restoration. Large-scale programs have beendeveloped for freshwater ecosystems which grade sites by comparing measured versus expected species richness.These models have been applied successfully to habitats that suffer from systemic pressures, such as poor waterquality. However, pressures in other habitats, such as rocky intertidal shores, are known to induce more subtlechanges in community composition.2. This paper tests a biodiversity assessment methodology that uses the ANOSIM R statistic to quantify the

biological dissimilarity between a site being assessed and a series of reference sites selected on the basis of theirsimilar environmental profile. Sites with high R values for assemblage composition have an anomalousassemblage for their environmental profile and are potentially disturbed.3. This methodology successfully identified moderate to heavily perturbed sites in a pilot study on 65 rocky

intertidal sites in south-eastern Australia. In general, measures based on percentage cover (flora and sessileinvertebrates) were more sensitive than abundance (fauna). Copyright r 2010 John Wiley & Sons, Ltd.

Received 19 August 2009; Revised 23 December 2009; Accepted 27 January 2010

KEY WORDS: biodiversity assessment; ecosystem health; intertidal; invertebrates; algae; dissimilarity; multivariate; ANOSIM

INTRODUCTION

Coastal marine habitats are affected by various humanactivities including habitat destruction and changedhydrology (e.g. for sea walls, boat ramps, marinas, etc.);water pollution (e.g. nutrients, toxins, salinity, temperature);

trampling or displacement by visitors; direct harvesting oforganisms for food, bait, aquaria or curiosity; introducedspecies; and climate change (Suchanek, 1994; O’Hara, 2002).

In response, marine managers frequently request standardizedbiodiversity assessment indices of site condition for ‘state ofenvironment’ reporting and to guide the allocation of scarce

management resources (Bailey et al., 2004). This information isoften required at large spatial scales on a limited budget.Typically managers want to rank site condition across the

entire jurisdiction of their agency.For example, the Natural Heritage Trust of the Australian

Government commissioned Museum Victoria to develop and

test a biodiversity assessment methodology to rank thecondition of at least 60 rocky intertidal sites across 1100 km

of coastline in the State of Victoria in temperate south-easternAustralia. This posed a number of methodological challengesthat would be typical of meso-scale projects in coastal

environments. First, there were no consistent quantitativemeasurements of environmental stressors across the study area.Monitoring of pollutants or nutrient enrichment was spatiallyconcentrated around a few sewerage outfalls and popular

beaches (O’Hara, unpubl. data). Measurement of recreationaluse was sporadic at best (Addison et al., 2008). Second, thebiological assemblages were potentially influenced by the range

of strong environmental (e.g. temperature, primary production,wave exposure, geology) and biogeographical gradients thatwere known to occur across the study area (O’Hara, 2000;

O’Hara and Poore, 2000; Underwood and Chapman, 2007).Third, the intertidal assemblages were likely to be characterizedby high spatial and temporal variability in species abundance,

*Correspondence to: Timothy D. O’Hara, Museum Victoria, GPO Box 666, Melbourne 3001, Australia. E-mail: [email protected]

Copyright r 2010 John Wiley & Sons, Ltd.

particularly at small (o1m) and regional (1–10 km) scales(Archambault and Bourget, 1996; Underwood and Chapman,1998; Fraschetti et al., 2005).

Consequently, without direct measurement of stressors, sitecondition had to be assessed solely from the composition and/or abundance of the fauna and flora, assuming that a change

will occur in the ecosystem in response to exposure to stressors(Bailey et al., 2004; Pinedo et al., 2007). However, the variationinduced by human stressors had to be distinguished from the

high environmental variability. The lack of stressormeasurements precluded partitioning environmental andimpact factors across a multivariate regression frameworksuch as distance-based linear modelling (McArdle and

Anderson, 2001). Moreover, pilot data suggested that asingle comprehensive multivariate analysis of all sites wouldbe unlikely to derive identifiable groups of sites with an

affected assemblage because of the presence of many strongenvironmental gradients across the large study area.

An alternative potential methodology was the reference

condition approach (RCA) developed for freshwater systemsthat uses ‘sites’ as the basic sampling unit, factors outenvironmental variation through the careful selection of

reference (control) sites, and measures the difference inbiological assemblage composition between test and referencesites as the basis for a preliminary assessment of site condition(Bailey et al., 2004). The RCA forms the basis for large-scale

programs to monitor freshwater systems in various countries,including RIVPACS in the United Kingdom (Wright, 1995;Clarke et al., 2003), AUSRIVAS in Australia (Simpson and

Norris, 2000) and BEAST in Canada (Reynoldson et al., 1995).However, there were potential problems in applying these

freshwater methodologies to coastal systems. Existing RCA

implementations develop an expected taxon list for a test sitebased on taxa found at the matching reference sites andcompute an observed over expected taxa (O/E) ratio for the

test site, which is used as an index of site condition. Not onlydoes this probabilistic presence/absence approach requirespecies-rich systems (Marchant et al., 1997), it also assumesthat environmental degradation will cause a loss of taxa at a

site (or at least the loss of the ability to detect them). However,on rocky shores, impacts such as trampling or harvesting areknown experimentally to induce subtle species-specific

responses in abundance rather than result in local extinction(Keough and Quinn, 1998, 2000). Models based on abundanceare likely to be more sensitive than models based on

presence–absence (Hewitt et al., 2005). Moreover, atmoderately polluted sites, nutrient enrichment can cause anincrease in species richness (Pearson and Rosenberg, 1978;Bishop et al., 2002). These difficulties can be avoided by using

a multivariate measure of the difference in assemblagesbetween a test site and a selected group of reference sites.One solution is to use the residuals from a PCA analysis or

multivariate regression as the basis of an index of disturbance(e.g. the CDI method, see Flaten et al., 2007). However,residuals reflect model error in addition to natural variability

and human disturbance. PCA in particular is rarelyappropriate for the non-linearities and zero-inflated datacommon in ecological studies (Clarke and Warwick, 2001).

Consequently it was decided to test a novel methodologythat used the non-parametric ANOSIM R statistic (Clarke,1993) to quantify the overall difference in assemblagecomposition between a test site and a series of reference

sites, which were quantitatively selected on the basis of theirsimilar environmental profile. ANOSIM R is based onsimilarity coefficients which can utilize abundance or

presence/absence data and make no assumption about thedirection of the assemblage change. Although ANOSIM R wasdeveloped as a test statistic to calculate the significance level of

multivariate differences between groups (Clarke, 1993), it is auseful comparative measure in its own right because it isderived from ranked rather than absolute similarity measures

and then scaled to lie between –1 and 1 (Clarke and Warwick,2001; Anderson et al., 2008). For example, comparativeANOSIM R measures are used to optimally split groups ofsamples in the linkage-tree procedure (Clarke et al., 2008).

Here we systematically calculate ANOSIM R values betweeneach site and its group of selected reference sites with a similarenvironmental profile, using these values to rank sites with

anomalous assemblages for further investigation.This biodiversity assessment methodology, hereafter called

MAVRIC (monitoring and assessment of Victoria’s rocky

intertidal coastline), was tested on both macro-invertebratefaunal and floral assemblages from 65 rocky intertidal sites insouth-eastern Australia, one of the largest intertidal surveys

conducted in the southern hemisphere. Sites ranged from beingputatively affected by sewage pollution (adjacent to outfalls) ortrampling (popular recreational sites) to relatively inaccessiblesites with no identified site-specific human impacts.

METHODS

The MAVRIC biodiversity assessment methodology

The MAVRIC biodiversity assessment procedure has two basicsteps: (1) selection of a set of reference sites for comparisonwith a test site on the basis of their shared environmental

characteristics (termed ‘nearest-neighbour’ sites after Linkeet al., 2004); and (2) calculation of ANOSIM R statistics forboth biological and environmental data, using the test site as

one group and the selected nearest-neighbour sites as thesecond group. These R values are a comparative measure of themultivariate assemblage difference between the test site and thenearest-neighbour reference set. High biological R values

indicate that the assemblage is clearly distinct from those atthe nearest-neighbour sites. The calculation of environmental Rvalues validates the reference site selection. High environmental

R indicates that the site was not well matched with the availablereference sites (see below). This can be visualized as a series ofordinations (Figure 1). A set of reference sites nearest to the

test site are selected from multi-dimensional environmentalspace (Figure 1(a)). The test site is then compared with this setof nearest-neighbour sites both environmentally (Figure 1(b))

and biologically (Figure 1(c)).ANOSIM R values are calculated by subtracting the mean

ranked similarity between pairs of the nearest-neighbour sitesfrom the mean ranked similarity between the test site and each

nearest-neighbour site, and the result then scaled to lie between�1 and 1 (Clarke and Green, 1988). Negative values of Rindicate that the test site cannot be distinguished from the

nearest-neighbour sites. Positive values of R indicate thatthe test site lies outside the range of variation encountered inthe set of nearest-neighbour sites. It is important to note that,

although the magnitude of the positive R values gives an

TESTING A RAPID BIODIVERSITY ASSESSMENT METHODOLOGY 453

Copyright r 2010 John Wiley & Sons, Ltd. Aquatic Conserv: Mar. Freshw. Ecosyst. 20: 452–463 (2010)

indication of how distinct the test site is from the nearest-neighbour group, negative values cannot be interpreted in thesame way, as these latter results are influenced by

compositional heterogeneity (‘clumpiness’) within thenearest-neighbour group itself rather than the relationshipbetween the test and nearest-neighbour sites.

It is more informative to consider the test/nearest-neighbour site relationship as falling into four groups basedon whether the R statistic is positive or negative for

environmental and assemblage data. Negative R values forboth indicate the test site has a suite of environmental factorsand biological composition within the variation found in thegroup of nearest-neighbour sites. Positive R values for

environmental data indicate that although the best-matchednearest-neighbour sites were chosen for comparison, the testsite still has a distinct environmental signature. The test site

may be unique, or inadequate reference sites surveyed. Thesetest sites can either have a positive or negative assemblage Rdepending on whether the unusual environmental conditions

are reflected in the biological assemblage. Sites with positiveassemblage R and negative environmental R have an unusualassemblage despite their typical environmental conditions.

These sites, particularly those with relatively high R (e.g.40.5), are the most interesting from a managementperspective and potentially disturbed by human activities.

Selection of reference sites

The accurate matching of test to nearest-neighbour sites isdependent on the selection of environmental predictors and the

number of nearest-neighbour sites chosen. The BIOENV

(Clarke and Ainsworth, 1993) or BEST (Clarke et al., 2008)procedures can be used to find the combination ofenvironmental variables that best match the assemblage

pattern (using the same non-parametric multivariateframework as ANOSIM) by correlating ranked similaritymatrices generated from both sets of data for all reference sites

using the Spearman coefficient. The use of ranked similaritycoefficients here avoids the requirement in some othermethodologies (RIVPACS and AUSRIVAS) to divide

reference sites into discrete ecological groups, when in realitythey form a continuum along various environmental gradients(Bailey et al., 2004; Dauvin, 2007). It also avoids the need touse ordinations (e.g. the ANNA model, Linke et al., 2004) or

cluster diagrams to match environmental predictors with thefaunal pattern which impose an artificial dimensionality on thedata (Clarke and Warwick, 2001).

Determining the number of nearest-neighbour sites to usein the comparison with a test site is more problematic. Smallnumbers of nearest-neighbour sites are likely to generate

variable results reflecting the lack of site replication. On theother hand, increasing the number of sites will increase theenvironmental heterogeneity within the nearest-neighbour

group which in turn will alter the likelihood that a test sitewill be considered distinct. This can be investigated empiricallyby treating each reference site in turn as the test site andcalculating the Environmental R for increasing numbers of

nearest-neighbour sites. The optimal solution has minimumstandard deviation (variability) and mean (distinctiveness) ofenvironmental R across all these relatively undisturbed sites.

A similar procedure was used to test the effect of varyingdata transformations of the biological abundance data(binary, fourth-root, log(x11), square root, and none) and

combinations of environmental variables. Finally, tests weremade to determine whether models based on selecting nearest-neighbour sites using the ‘best’ match of environmental

variables performed better (i.e. lower mean and standarddeviation of environmental R) than selecting sites based (1)solely on latitude/longitude, or (2) chosen at random withoutreplacement.

Species analyses

The percentage contribution of each species to the assemblageBray–Curtis dissimilarity between test and nearest-neighboursites can be determined by using the SIMPER (similarity of

percentages) procedure (Clarke, 1993). The percentagecontributions of individual species were aggregated intobroad taxonomic groups for interpretability, by (1) assigning

a positive value to each species contribution if the averageabundance was greater at test sites and a negative value ifgreater at the reference sites, and (2) summing the overallcontributions for each taxonomic group. The groups were (a)

for sessile taxa: blue-green, green, brown and red algae;lichens; bivalves; serpulid and spionid polychaetes; ascidians,and (b) for motile taxa: anemones; chitons, bivalves,

gastropods, pulmonates; barnacles, crabs; seastars; ascidians.

Data sets

A survey of intertidal rock platforms was conducted along the1100 km Victorian coast in south-eastern Australia (Figure 2).

Fifty-eight reference sites were surveyed between February and

Reference site

Test site

Stress: 0.08

Stress: 0.1Stress: 0.01

Environmental data

Environmental data Faunal data

(a)

(b) (c)

Figure 1. The MAVRIC concept. Reference sites that areenvironmentally most similar to a test site are identified usingdissimilarity coefficients. The test site is then comparedenvironmentally and biologically with the selected reference sitesusing the ANOSIM R statistic, which for this purpose subtracts themean ranked similarity between pairs of reference sites from thatbetween the test site and each reference site and then is scaled to liebetween �1 and 1. This can be visualized as a series of ordinations. Inthis example (a) eight reference sites nearest environmentally to the testsite (Pickering Point) are identified; the test site (b) falling within therange of variation of these reference sites environmentally (ANOSIMR5�0.22) but (c) having a distinct faunal assemblage (R5 0.74).

T. D. O’HARA ET AL.454


May (autumn) 2005. The sites were selected from prior fieldexperience on the basis of geographic spread, size and

accessibility. Inaccessible (relatively unimpacted) sites weresurveyed where possible, including some on offshore islandsand others without ready vehicular and/or pedestrian access.

Several sites were within marine protected areas established inNovember 2002. On the more accessible sections of thecoastline, sites were chosen that were furthest from

pedestrian access points. Eleven of these sites were re-sampled between February and March 2006 in order tomeasure temporal variability within reference sites.

An additional eight test sites were selected for theirvulnerability to (a) pollution from sewerage outfalls, or(b) disturbance by anthropogenic recreational use. One site(Boags Rocks) was surveyed immediately adjacent to a major

sewerage outfall and another two were 0.5 (Boags East) and3.2 km (Fingals Beach) to the east. This outfall discharges370ML day�1of effluent from the Eastern Treatment Plant,

which treats 42% of the sewage from Melbourne, the capitalcity of Victoria (Newell et al., 1999). A fourth putativelypolluted site was surveyed adjacent to a smaller outfall at

Pyramid Rock, which discharges 0.6MLday�1 of effluentfrom Phillip Island. Consistent data for coastal visitation donot exist across Victoria, however, four test sites were chosen

on the basis of their likely (Point Grey, Barwon Heads, PointLonsdale) or known (Sorrento, see Addison et al., 2008) highrates of recreational use. Test sites were surveyed betweenFebruary 2005 and April 2007.

Biological data

Sites were surveyed during daylight hours, on days where thepredicted low tide was between 0 and 0.4m above datum. Ateach site, one 50m wide location was chosen, and two 10m widetransects were surveyed within each location. Along each

transect, a randomly placed group of five quadrats(50� 50 cm) were surveyed within three 5� 5m areas, one ineach of the high, mid and low shore levels. In the absence of

accurate height-above-shore data (see below), shore levels weredetermined visually by the following criteria (Bennett and Pope,1953): high shore—dominated by littorinid snails (mainly

Austrolittorina unifasciata); mid shore—dominated by mussels

(e.g. Xenostrobus pulex and Brachidontes rostratus), gastropodlimpets (e.g. Cellana tramoserica and Siphonaria diemenensis)

and gastropod snails (e.g. Chlorodiloma odontis and Bembiciumnanum); low shore—dominated by algae (e.g. Hormosirabanksii). The kelp-dominated fringes at the reef edge and rock

pools (40.1m deep) were considered subtidal and not surveyed.The abundances of each macro-invertebrate species

(44mm) were counted for each quadrat (the ‘faunal’

dataset). Dense aggregations of barnacles, mussels andlittorinid molluscs were occasionally estimated from foursubquadrats (62.5� 62.5mm). The percentage cover of

macroalgae and sessile invertebrates (particularly mussels,barnacles, tubicolous polychaetes, ascidians) were alsoestimated for each quadrat using a 50 point grid (the ‘cover’dataset). Some species that were difficult to identify in the field

(particularly filamentous and encrusting algae) were aggregatedinto higher taxa. The mean quadrat abundance and/or coverfor each resulting taxon were then calculated for each site.

Environmental predictors

In order to calculate environmental data for each site, ageographic information system (GIS) was constructed usingArcGIS 9.0 software (ESRI, 2004) from (a) environmental

data derived from oceanographic and climate models, (b) geo-referenced, ortho-rectified and mosaicked aerial (1.2m pixel)and satellite (1m pixel) imagery, and (c) survey data.

While it would have been preferable to use quantitativein situ rather than modelled measurements of many variables(e.g. for wave energy see Helmuth and Denny, 2003), such data

sets were not available for the Victorian coasts and werejudged to be prohibitively expensive to collect. Consequently,the best available modelled or qualitative data sets were used.However, the use of environmental factors in the methodology

was not to build a comprehensive predictive model of howthese factors influence assemblage composition, but only toimprove the selection of nearest-neighbour sites. The non-

parametric environmental selection methodology used here(BIOENV, see above) selects the set of variables that ‘bestmatch’ the overall assemblage pattern regardless of their

resolution or collinearity.

Figure 2. Map of the survey sites and bioregions defined by the Interim Marine and Coastal Regionalisation for Australia (IMCRA) (Thackway andCresswell, 1998). Named sites are discussed in the text.



Oceanographic data were derived from the CSIRO Atlas ofRegional Seas (CARS2000) data sets (Ridgway et al., 2002;Commonwealth of Australia, 2005) and included mean sea-

surface temperature, primary production, salinity, phosphate,nitrate, silica, and oxygen. Primary production was derived fromsatellite observations of ocean colour data, a surrogate of

phytoplankton concentrations in surface waters (Commonwealthof Australia, 2005). Data for each survey site were obtained byspline interpolation from the original raster datasets (which

included coastal areas) with resolutions of 0.02–0.0431. Rootmean square wave height (Hrms) and wave period (T) werederived from three years (30/07/2002 to 30/07/2005) of diurnalpredictions made by a wind-driven offshore model with a

resolution of 0.1251 latitude/longitude (Australian Bureau ofMeteorology), combined into a wave power variable using theformula P5 (rG2/4p)(Hrms

2 /8)T, where r is the density of water

and G the gravitational constant (Leigh et al., 1987), andextrapolated to the coastline using ordinary Kriging with thesemi-variogram fitted to a spherical model.

To determine whether the site faunal assemblage wasinfluenced by a species–area effect (Schoener, 1976), the areaof intertidal reef was calculated in three ways, the size of the

immediate reef (bounded by significant areas of sand or mud),the amount of available reef within a 1 km radius, and theamount of reef within a 10 km radius. These values werecalculated by manually digitizing intertidal reef from aerial

and satellite imagery (1 m pixel size) and ground-truthing areasof uncertainty using methodologies based on Congleton et al.(1999) and Zharikov et al. (2005). The amount of reef within a

radius was calculated using the ‘Extract by Circle’ analysis inArcGIS 9.0 (ESRI, 2004).

The rock type for each site was categorized from geological

site descriptions (Bird, 1993) and then transformed into anordinal variable using the qualitative Moh’s scale of hardness(in general, granite 4 basalt 4 calcarenite 4 limestone 4sandstone). Sites were classified in the field into two additionalbinary variables, reef type (boulder reefs or flat rock platforms)and coastal type (bay or open coast). The aspect of each reefwas derived from the perpendicular angle to the shoreline (e.g.

05north, 1805 south) determined from aerial imagery.At the smallest scale, reef rugosity, latitude and longitude

were measured for each quadrat. Rugosity was measured by

trailing a 3mm wide chain over the surface of the reef undertwo perpendicular sides of each quadrat and measuring thetotal extended length once removed. Latitude and longitude

were taken from portable Garmin 72 GPS units. OrdinaryGPS-derived altitude was found to be inaccurate at the scalesrequired (0–3m above sea level) and not used. For thesefactors, quadrat values were averaged (mean) to calculate a site

centroid value.

Data transformations and similarity coefficients

The abundance data were transformed to ensure that analyseswere not dominated by a few common data. The spread of

mean abundances (0–3,504) and mean percentage cover (0–17)suggested a severe (fourth-root) and a moderate (square-root)transformation, respectively. However, ANOSIM R values

derived from binary, fourth-root, log(x11), square root, anduntransformed data were generated for comparison (seeabove). All analyses used the Bray–Curtis similarity for

biological data and double-scaled Euclidean distance for

environmental data. This latter metric is calculated bylinearly scaling the data to lie between 0 and 1 (using theminimum and maximum data values), and then scaling the

resultant Euclidean distance to also lie between 0 and 1 bydividing by the square root of the number of variables.

RESULTS

Assemblage patterns

In total 96 macro-invertebrate species were identified andcounted from the 58 reference sites, including six anemones,

eight chitons, seven bivalves, one opisthobranch, 42prosobranch gastropods, four pulmonates, nine barnacles,nine crabs, one brachiopod, five seastars, two sea cucumbers,

one brittlestar and one ascidian. An MDS ordination of site/mean species abundance (Figure 3(a)) showed embayment sitesdispersed to the right, largely differentiated from those on theopen coast to the left. The open coast sites formed a broad

geographical gradient, with those from the east (Twofold andFlinders bioregions; Thackway and Cresswell, 1998) formingdistinct groups at the bottom of the ordination. Sites from the

two western bioregions were not as clearly differentiated.SIMPER analyses revealed that the distinction between

embayments and open coast was primarily driven by a shift in

barnacle and gastropod species. Along the open coastChamaesipho, Chthamalus and Catomerus were the mostcommon barnacle genera (contributing 16.4% of the totaldissimilarity), whereas Tetraclitella and Elminius were the most

common in embayments (6.6%). The littorinids Austrolittorinaand Afrolittorina, generally abundant in the wave splash zonealong the open coast, were much less common in embayments

(7.8%). Embayments also had fewer limpets (e.g. Cellana,Notoacmea, Siphonaria, in total 16.8%) but more trochids (e.g.Austrocochlea, 6.1%). The difference in east (Twofold,

Otway

Central Victoria

Flinders

Twofold Shelf

Victorian Embayments

Stress: 0.16

Otway

Central Victoria

Flinders

Twofold ShelfVictorian Embayments

Stress: 0.21

(a)

(b)

Figure 3. MDS ordinations of (a) faunal abundance and (b)percentage cover from the 58 reference sites, grouped by bioregion

(see Figure 2).



Flinders) and west (Otway, Central Victoria) along the opencoast was driven principally by differences in relativeabundance of a range of species rather than biogeographical

turnover. Barnacles (25.0%) and ascidians (Pyura, 3.3%) weregenerally more common in the east and littorinids(Austrolittorina, Afrolittorina, 6.1%) in the west. Species

contributing more than 1% to the dissimilarity betweeneastern and western bioregions that were known to have adistributional limit within Victoria included the western

gastropods Nerita atramentosa (2.2%), Notoacmea mayi(1%), Chlorodiloma odontis (1%), C. adelaidae (1%) and theeastern Nerita melanotragus (2.9%) and the chitonSypharochiton pelliserpentis (1%).

In total 59 species were counted as percentage cover,including two mytilid bivalves, one ascidian (Pyura), twoaggregating polychaetes (the spionid Boccardia and the

serpulid Galaeolaria), 10 green algae, 26 brown algae, 15 redalgae, two blue-green algae (Rivularia) and one lichen(Lichenia). An MDS ordination of these data (Figure 3(b))

showed less bioregional differentiation. In particular,embayment sites were not differentiated, even in theequivalent 3D ordination (not shown).

Reference site selection

The BIOENV analysis selected six environmental predictors asbest matching the overall biotic pattern for all reference sites:rock hardness, wave power, primary production, salinity,

phosphate and the binary bay/open coast variable (Spearmanr5 0.63 for macro-invertebrates, r5 0.32 for cover). Theresults were slightly improved for cover by adding oxygen(r5 0.34) rather than salinity and phosphate, however, to

ensure comparability, the faunal set of predictors were used forboth data sets. Predictors based on latitude, longitude, reefarea, sea surface temperature and rugosity were not

emphasized in any of the top BIOENV groupings.The number of nearest-neighbour sites to be used in the

model was determined empirically by treating each (relatively

undisturbed) reference site in turn as a test site, andinvestigating the behaviour of the MAVRIC model. Sitesresponded differently to increased numbers of nearest-neighbour sites (Figure 4(a)). Some sites (e.g. Cat Bay) were

well matched environmentally to a few others, but thenbecome increasingly distinct as more environmentally remotesites were added to the comparison, increasing the

environmental ANOSIM R. Other sites (e.g. Cape Paterson)were distinct initially but gradually became indistinguishableas the size of the comparative reference group was increased.

Others (not shown) were highly volatile initially but trended toa stable value of R with increased numbers of sites or wererelatively stable throughout. The optimal number of nearest-

neighbour sites to be used was determined from the loweststandard deviation of environmental R across all 58 referencesites, i.e. the number where most reference sites were notdifferentiated environmentally from their nearest-neighbour

group. This was eight sites for this data set (Figure 4(b)).Faunal R values for all reference sites were compared with

a null model (Figure 4(c)), where nearest-neighbour group for

each site was selected at random and the resulting R averagedover 100 iterations. The MAVRIC model had lower standarddeviation than the null model for fewer than 20 nearest-

neighbour sites and lower mean for 4–14 sites. MAVRIC cover

R had lower standard deviation for 4–9 sites and lower meanfor 4–40 sites (not shown). A spatial model, based solely onlatitude and longitude as environmental predictors (notshown), also had higher standard deviation and mean of R

than the MAVRIC model for both faunal abundance andpercentage cover. The spatial model performed more poorlythan the null model for large numbers of nearest-neighbour

sites (420).A similar procedure was used to assess the affect on

ANOSIM R of varying the severity of the faunal and cover

data transformation from none, square root, log(x11), fourthroot to binary. Only the binary transformation had a notableeffect on the standard deviation and mean of the R values (not

shown), generating higher values (i.e. a poorer model) thanabundance transformations.

Test sites

The environmental and biotic R were generated from eightnearest-neighbour sites for all 58 reference and eight putativepolluted or trampled test sites (Figure 5). Sixty of the sites had a

negative environmental R indicating a good environmental match

Null Model (100 iterations)Cape Paterson

Cat Bay

5

Number of nearest-neighbour sites

-0.8

(a)

(b)

(c)

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

Null Model (100 iterations)Standard deviation

Mean

Number of nearest-neighbour sites

-0.3

-0.1

0.1

0.3

0.5

0.7

MAVRIC modelStandard deviation

Mean

Null Model (100 iterations)Standard deviation

Mean

Number of nearest-neigbour sites

-0.3

-0.1

0.1

0.3

0.5

0.7

Env

ironm

enta

l AN

OS

IM R

Env

ironm

enta

l AN

OS

IM R

Faun

al A

NO

SIM

R

10 15 20 25 30 35 40

5 10 15 20 25 30 35 40

5 10 15 20 25 30 35 40

Figure 4. Trends in ANOSIM R values with increasing number ofreference sites. (a) Two example sites showing how they can becomeincreasingly (higher R) or decreasingly environmentally differentiated asmore nearest-neighbour sites are included in the model. (b) The meanand standard deviation of environment R values for all 58 referencesites. Optimal models have both low standard deviation and mean. (c)For faunal abundance, the MAVRIC model (selecting reference sitesbased on their similar environmental characteristics) performed betterthan a null model (selecting sites at random) for less than 20 sites.



between the test site and their nearest-neighbour group. Theexceptions were reference sites in embayments (e.g. DoughboyIsland in Corner Inlet) and the far-east of Victoria (e.g. Gabo

Island). These were uncommon habitats within the study area andconsequently were not well matched environmentally to theselected group of eight nearest-neighbour sites.

Of the sites with negative environmental R, 36 also had

negative faunal R indicating a good correspondence betweenenvironmental predictors and the faunal assemblage for these

sites (see Q3 on Figure 5(a)). Relatively few of these sites hadlarge positive values of faunal or cover R (Q1). For example,only four had a faunal R over 0.5 (Figure 5(a)) and six had a

cover R over 0.5 (Figure 5(b)).The eight putative polluted or trampled test sites all had

negative environmental R but varying faunal and cover R

(Figure 5). Two polluted (Boags Rocks R5 0.7, Boags EastR5 0.1) and one trampled site (Barwon Heads R5 0.1), hadpositive faunal R; and three polluted (Boags Rocks R5 0.98,

Boags East R5 0.77, Fingals R5 0.64) and one trampled site(Sorrento R5 0.38) had positive cover R.

The taxa that were driving the large (40.5) faunal andcover R values were investigated using SIMPER analyses

(Tables 1 and 2). For the putatively-heavily polluted site atBoags Rocks, there was a clear decline in most faunal andcover groups compared with the nearest-neighbour sites, most

notably gastropods, barnacles, brown (Hormosira) and redalgae, and an increase in spionid polychaetes (Boccardia),green algae (Ulva), anemones (particularly Aulactinia), and

bivalves (Brachidontes, Lasaea). The nearby site of Boags Eastalso had reduced brown algal cover, but lacked the spionidsand had above average green (Ulva) and red (foliose coralline

and fleshy turfing) algae. A few kilometres to the east, atFingals Beach, the dense algal turf included the brown algaCapreolia implexa, foliose coralline and fleshy red algae (e.g.Laurencia). The dominant macro-brown alga Hormosira was

present, although with a low percentage cover.The three reference sites with high cover ANOSIM R

varied. Childers Cove and Secret Beach lacked the typical

Hormosira cover, the former site having above average greenalga Caulerpa fragilis and the latter having above averageLaurencia, foliose corallines, encrusting corallines and Ulva.

A repeat visit to Secret Beach indicated that Hormosira waspresent but only in pools scattered among boulders, none ofwhich were in the paths of the surveyed transects. Cape

Paterson on the other hand differed in having fewer mussels(Xenostrobus) and a greater diversity of brown algae (includingCystophora, Scytosiphon and Halopteris) that were generallyrestricted to deeper rock pools at other sites.

The three reference sites with high faunal ANOSIM R alsovaried. Settlement Point, an embayment site surrounded bymuddy sediments, had an odd assemblage with relatively few

gastropods, but higher numbers of crabs, chitons andechinoderms (and a few specimens of other unusual groupssuch as brachiopods). Pickering Point had relatively few

molluscs or barnacles, but more anemones and seastars.Urquhart Bluff had reduced numbers of anemones,pulmonates and barnacles but a typical gastropod assemblage.

Temporal differences

A comparison of the test sites with a subset of 11 reference sitesfrom central Victoria surveyed for two consecutive yearsshowed some stability in the pattern for both percentage coverand faunal abundance between surveys (Figure 6). A few sites

had a substantially changed R (40.3) between year 1 and year2 including (a) for fauna, the test site Boags East (�0.4) andthe reference sites Point Addis (10.6), Point Roadknight

(�0.4), Flat Rocks (10.6); and (b) for cover, the reference sitesCape Paterson (�0.8) and Flat Rocks (10.5). However, withthe exception of percentage cover at Cape Paterson, the sites

with negative environmental R and assemblage R40.5

-1.0

Environmental ANOSIM R

-1.0

(a)

(b)

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Faun

al A

NO

SIM

R


-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Cov

er A

NO

SIM

R

Boags Rocks

Pickering

Point

Urquharts Bluff

Settlement

Point

Doughboy Is

Q1

Q4Q3

Q2

Pyramid

Rocks Point Grey

Sorrento

Fingals

Point LonsdaleBoags East

Barwon Heads

Q2Boags Rocks

Q1

Q4Q3

Secret Beach Cape Paterson

Childers Cove

Gabo Is

Boags East

Fingals

Sorrento

Barwon Heads

Point Grey

Point

Lonsdale

Pyramid Rock

-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Figure 5. Plot of ANOSIM R values for 58 reference (solid circles) andeight test sites (open squares) generated by the MAVRIC procedure fromenvironmental predictors and (a) faunal abundance, and (b) percentagecover. Quadrant 1 (Q1)5Sites that are well matched environmentallywith their nearest-neighbour sites but have an anomalous assemblage.Q25Sites with an anomalous environmental profile and assemblage.Q35Sites with an environmental profile and assemblage that cannot bedistinguished from their nearest-neighbour set. Q45Sites with ananomalous environmental profile but with an assemblage that cannot

be distinguished from the nearest-neighbour sites.



Table1.SIM

PER

percentagecontribution

totheassem

blagedissimilarity

ofcover

between

selected

test

and

eightnearest-neighboursitesaccumulated

into

majorbiotic

components.

Site

Sitetype

ANOSIM

RAccumulatedSIM

PER


Environment

Cover

Blue-green

algae

Lichens

Green

algae

Brown

algae

Red

algae

Bivalves

Serpulid

polychaetes

Spionid

polychaetes

Ascidians

BoagsRocks

Testsite

(polluted)

�0.38

0.98

�1.4

�4.2

9.2

�26.4

�12.4

3.7

�6.0

19.3

4.2

BoagsEast

Testsite

(polluted)

�0.38

0.77

�1.3

�3.8

20.0

�9.8

21.7

�6.7

4.5

0.0

5.3

Fingals

Testsite

(polluted)

�0.34

0.64

�0.7

�2.9

10.0

4.7

26.4

�5.2

2.6

0.0

0.0

ChildersCove

Reference

site

�0.53

0.63

�3.3

3.4

7.5

�26.9

�13.9

�1.0

�7.8

0.0

0.0

CapePaterson

Reference

site

�0.05

0.53

4.3

�4.3

2.7

15.0

3.5

�15.1

�3.1

0.0

�0.5

SecretBeach

Reference

site

�0.13

0.58

�1.3

8.0

7.8

�25.3

18.5

�11.7

3.3

0.0

�3.0

Positivepercentagecontributionsindicate

thattheaveragepercentagecover

(square-roottransform

ed)is

greateratthetest

site,negativecontributionsare

thereverse.Only

siteswithanegative

EnvironmentalR

andCover

R40.5

are

listed.

Table2.SIM

PER

percentagecontributionto

theassem

blagedissimilarity

offaunalabundance

betweenselected

test

andeightnearest-neighboursitesaccumulatedinto

major

bioticcomponents.

Site

Sitetype

ANOSIM

RAccumulatedSIM

PER


Environment

Fauna

Anem

ones

Chitons

Bivalves

Gastropods

Pulm

onates

Barnacles

Crabs

Seastars

Ascidians

Settlem

entPoint

Reference

site

�0.16

0.78

�2.8

4.1

5.3

�20.8

�3.9

�3.1

10.1

1.6

�0.5

PickeringPoint

Reference

site

�0.22

0.74

2.8

�4.1

0.0

�39.2

�8.5

�10.7

�3.1

3.9

0.0

BoagsRocks

Testsite

(polluted)

�0.38

0.72

8.5

�3.6

4.7

�32.4

�8.5

�9.8

�3.7

�3.2

4.0

Urquhart

Bluff

Reference

site

�0.14

0.66

�8.0

0.8

�0.7

0.4

�4.1

�5.4

�0.7

7.0

�2.2

Positivepercentagecontributionsindicate

thattheaverageabundance

(double

square-roottransform

ed)is

greateratthetest

site,negativecontributionsare

thereverse.Only

siteswithanegative

EnvironmentalR

andFaunalR4

0.5

are

listed.



remained the same for the two years. A SIMPER analysis ofthe change in percentage cover at Cape Paterson (not shown)

indicated that a more typical assemblage was surveyed in Year2 than the unusual assemblage in Year 1 (see above), withincreased cover of the brown alga Hormosira and reduced

cover of rock-pool brown algae being recorded.

DISCUSSION

The RCA as an exploratory tool

This study tested a rapid RCA-type biodiversity assessmentmethodology that used the ANOSIM R statistic to quantifythe biological and environmental dissimilarity between a site

being assessed and a series of reference (nearest-neighbour)sites objectively selected on the basis of their similarenvironmental profile. These dissimilarity results were then

compared with a threshold (for example R40.5) to identify

sites that have an assemblage that was clearly distinct from thevariation shown by the selected nearest-neighbour sites. This

system has advantages over existing meso- to large-scale RCAprograms in not relying on the artificial categorization of sitesinto habitat groups, being able to detect changes in abundance

rather than testing for species gain or loss, and not makingassumptions about the direction of assemblage change.

This study successfully identified anomalous assemblages atsites that were putatively identified as moderately to heavily

polluted by sewage. One site in particular (Boags Rocks) had avery distinct assemblage measured using either faunalabundance or floral/faunal percentage cover. Other

comparative studies have described reduced brown algal(Hormosira) cover and mats of the spionid worm Boccardia(Brown et al., 1990; Bellgrove et al., 1997) at this site. Two

adjacent sites (Boags East and Fingals) also had very distinctassemblages based on floral/faunal percentage cover but notfaunal abundance. Conversely, assemblages at sites known to

be subject to medium to high levels of visitor pressure were not

Year 1

-1.0


-1.0

(a) (b)

(c) (d)

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Faun

al A

NO

SIM

RYear 2


-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Faun

al A

NO

SIM

RYear 1


-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Cov

er A

NO

SIM

R

Year 2


-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0C

over

AN

OS

IM R

Boags Rocks

Flat Rocks

Point Addis

Point Roadknight

Point Grey

Fingals

Barwon Heads

Boags East

Sorrento

Pyramid Rock

Boags Rocks

Flat Rocks

Point Roadknight

Point Addis

Point Grey

Pyramid

Rock

Sorrento

Barwon Heads

Boags

East

Fingals

Boags Rocks

Boags East

Sorrento

Fingals

Pyramid Rock

Barwon

HeadsPoint Grey

Cape Paterson

Flat Rocks

Pyramid Rock

Boags Rocks

Boags East

Fingals

Sorrento

Barwon

Heads

Point Grey

Cape Paterson

Flat Rocks

-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Figure 6. Comparison of (a, b) faunal and (c, d) cover ANOSIM R between consecutive survey years for 11 reference and seven test sites in centralVictoria. Open squares represent test sites and solid circles reference sites.



distinguished, although that may be an artefact of the spatialscale of the sampling design (see below).

Apart from the known polluted sites, sites with ‘distinct’

assemblages fell into two other groups. The first group wascharacterized by large environmental as well as assemblagedissimilarity. They represented unique or marginal habitats

which were not well matched environmentally to other sites.These environmental ‘outliers’ (e.g. Doughboy Is, Gabo Is)should be removed from operational implementations if

additional reference sites cannot be found.The second group had distinct assemblages even though

a priori they were not understood to be impacted byanthropogenic activities and were environmentally well

matched with their nearest-neighbour sites. These sitesclearly warrant further study. For example, the depauperatefaunal assemblage at Pickering Point warrants further

management attention, as, although it is in a marine park,the nearby Merri River estuary has been extensively modifiedby human activities, the Warrnambool city sewage outfall is

only 500m to the west, and during summer the site is subject toheavy visitation (Parks Victoria, 2006).

It is important to emphasize that this approach is

exploratory, identifying assemblage outliers, those sites thatvary more than expected from background variation. It shouldbe used as an exploratory management tool. It does notidentify causal relationships between assemblage composition

and anthropogenic threats.

Model refinement

In this study, assemblages at sites known to be subject tomedium to high levels of trampling or low levels of sewage

were not distinguishable from the nearest-neighbour referencessites. Other studies have reported similar or variable findings.The lack of sensitivity in this study may be related to the

definition of a site (50m of coastline) that masks the localizedscale or spatially heterogeneous effect of these impacts(Keough and Quinn, 2000; Bishop et al., 2002). For example,

even though repeatedly trampled ‘paths’ could be clearly seenat Sorrento they were not of sufficient scale to alter averageassemblage composition across the whole site. If small-scaleimpacts are of management interest, smaller sampling units

may be required.This survey measured both percentage cover and faunal

abundance. Both have advantages and disadvantages. Faunal

abundance was better matched environmentally to theavailable predictors. However, it appeared relativelyinsensitive to anthropogenic disturbance, with only the

highly polluted test site at Boags Rocks being identified asdistinct. Cover showed less obvious environmental pattern,possibly because fewer taxa could be consistently identified to

species in the field (particularly encrusting coralline andfilamentous algae). Conversely, cover appeared moresensitive to anthropogenic disturbance, particularly the lossof brown macro-algae such as Hormosira. Abundance models

performed better than those based on presence–absence.The selection of nearest-neighbour sites using a suite of

environmental variables, chosen with the BIOENV procedure,

resulted in superior models (lower overall assemblagevariation) than models based on spatial coordinates alone orrandom selection of sites. This does not imply that these

environmental variables alone are driving assemblage

composition, only that sites with similar values for thesevariables also share a relatively consistent flora and fauna.

Applications

The methodology tested here is suitable for meso- to large-scale assessments of ecosystem condition, particularly forsystems with strong ecological and biogeographic gradients,and/or when there is an absence of quantitative impact data.

The methodology will identify sites that have anomalousassemblages compared with a selected group of sites withsimilar environmental profiles. This is a rapid biodiversity

assessment methodology that will identify moderately toheavily perturbed sites at relatively low cost, particularly ifcareful attention is given to the scale of sampling compared

with the scale of expected impact. Anomalous sites can then beinvestigated further to determine the underlying causes of themeasured dissimilarity. Given the inadequate funding of many

environmental and conservation agencies, the focus onmoderately to severely affected sites is unlikely to be animpediment to management action (Bottrill et al., 2008).

A database (Microsoft Access 2007r) with the biological/

environmental data, and software to run the calculations, isavailable from the corresponding author on request.

ACKNOWLEDGEMENTS

The project was funded by grants from the AustralianNational Heritage Trust (NHT Project no: 202244), ParksVictoria, and the Victorian Environmental Protection

Authority. Special thanks to Dr Richard Marchant (MuseumVictoria) for many statistical discussions and explaining theunderlying methods of RIVPACS and AUSRIVAS models;

the late Clarrie Handreck (Marine Research Group ofVictoria) for his invaluable help is selecting field sites andidentifying problematic animals; Liz Greaves, Rebecca Koss

and Anna McCallum (Museum Victoria) for assisting withfield work; Dr Anthony Boxshall and numerous rangers (ParksVictoria) for facilitating access to remote sites; and twoanonymous reviewers for their insightful comments.

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a rapid biodiversity assessment methodology tested on intertidal rocky shores

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