SUMMARY

Species are identified as bioindicators when their pres-ence or abundance within sites of a particular habitator a conservation area are high. Dufrêne and Legendre(1997), Ecological Monographs 67: 345–366, proposed theuse of a flexible asymmetrical approach to detect indi-cator species and species assemblages characterizinggroups of sites, the indicator value for each speciesbeing subsequently tested by randomization. Althoughthe advantages of these methods in comparison toother multivariate or two-way indicator speciesanalyses have been demonstrated, relatively fewecological studies have made use of them. The aim ofthe present study was to demonstrate the advantagesof the indicator species test using a data set generatedby traditional fisheries activities at four areas in theBonifacio Strait Marine Reserve, Corsica, France.ANOVA, Kruskal-Wallis and another randomizationtest were also performed on the same data set. Of the54 species caught in the fishing nets, the indicatorspecies analyses identified 19 species for which theindicator value was significant for a given area,whereas the a posteriori Tukey test for ANOVAdetected only 12 species for which the mass captureper unit effort was significantly greater for a givenarea. The efficiency of this new approach within theframework of conservation and marine reserve studiesis demonstrated by focusing on three species (Sciaenaumbra, Palinurus elephas and Maja verrucosa).

Keywords: artisanal fisheries, bioindicators, indicator speciesanalysis, fish, marine protected areas, randomization areas

INTRODUCTION

To assess the effects of area protection or habitat character-istics on species biomass or density, ANOVA-type ornon-parametric tests are frequently used. In marine ecology,the most used methods involve either the sampling of speciesboth within and outside of the target sites (Rakitin & Kramer1996; Chapman & Kramer 1999; LaMesa & Vacchi 1999;McClanahan & Arthur 2001; Tratalos & Austin 2001;

Guidetti et al. 2002) or the monitoring of populations withinsites before and after protection in before-after-control-impact (BACI) designs (McClanahan & Kaunda-Arara 1996;Pipitone et al. 2000; Jennings 2001). Biomasses of fish, be thedata gathered by fishing or by visual census, exhibit distribu-tions that are often far from being normal and contain a largeproportion of zero scores (Mouillot et al. 1999; Mouillot &Culioli 2002), a condition that can affect both ANOVA andnon-parametric tests (Manly 1998). In addition, the extensivevariability of the data renders both the application conditionsand interpretation of the classical tests employed rather prob-lematic (Rice 2000).

A number of authors have sought to determine therelationships between areas and species, in other wordsidentify species as bioindicators of a given ecosystem (Stork &Samways 1995). A species is an indicator when it characterizesa group of sites; it is found mostly in a single group andpresent in the majority of the sites belonging to that group(sensu Dufrêne & Legendre 1997). In the context of environ-mental conservation, groups can be areas with the samehabitat or the same management. The methods used aregenerally multivariate analyses or more specific methods forindicator species such as two way indicator species analysis(TWINSPAN; Hill 1979). Many of these methods sufferfrom methodological problems, including the tendency toselect rare species as being distinctive of particular habitats(Clarke 1993), the identification of a strong gradient pervadingthe data structure in TWINSPAN (Belbin & McDonald1993), or bias in assessing relative abundance of species that islinked to both the relative abundance of a species being depen-dent on the absolute abundance of other species, and samplingefficiency being species dependent (Dufrêne & Legendre1997; McGeoch & Chown 1998). This is particularly true forfishing data from the marine environment (Rice 2000).

Dufrêne and Legendre (1997) introduced a new and flex-ible asymmetrical approach to identify both indicator speciesand species assemblages characterizing groups of sites. Thisnew method was called asymmetric because species areanalysed on the basis of a prior partition of sites (Legendre &Legendre 1998) and is based on an indicator value index(IndVal). The ecological advantages of this method in conser-vation biology have been highlighted by Legendre andLegendre (1998) and by McGeoch and Chown (1998). Thus,a randomization procedure is used to test the statisticalsignificance of species’ indicator values (Dufrêne & Legendre

Indicator species analysis as a test of non-random distribution of species inthe context of marine protected areas

DAVID MOUILLOT 1 *, JEAN-MICHEL CULIOLI 2 AND THANG DO CHI 1

1UMR CNRS–UMII 5119 ECOLAG, Université Montpellier II-cc 093, 34095 Montpellier Cedex 5, France, and 2Réserve Naturelle desBouches de Bonifacio, Office de l’Environnement de la Corse, Avenue Jean Nicoli, 20 250 Corte, FranceDate submitted: 14 November 2001 Date accepted: 1 July 2002

* Correspondence: Dr David Mouillot e-mail: mailto:mouillot@crit.univ-montp2.fr

Environmental Conservation 29 (3): 385–390 © 2002 Foundation for Environmental Conservation DOI:10.1017/S0376892902000267

1997). Despite the obvious value of this approach, the IndValtest is not widely used in ecology (see Hufnagel et al. 1999;Detsis et al. 2000; Zimmer et al. 2000; Mikusinski et al. 2001;Renjifo 2001; Chytry et al. 2002).

The aim of the present study was to evaluate the advan-tages of the indicator species analysis to detect non-randomdistributions of species between areas and to identifyspecies-habitat relationships. Using as an example a data setgenerated by traditional fisheries at four areas in theBonifacio Strait Marine Reserve in Corsica, France, wesought to evaluate the efficiency of this new method withinthe framework of marine area conservation and manage-ment.

METHODS

Data collection

The data used were those generated by traditional fishingusing trammel nets in the Bonifacio Straits Natural Reserve,Corsica, France. This Natural Reserve was created inSeptember 1999 and encompasses the Lavezzi IslandsReserve created in 1982 (Culioli 1994; Mouillot et al. 1999;Badalamenti et al. 2000; Mouillot & Culioli 2002). We separ-ated this reserve into four areas in our study (Fig. 1), area 4

corresponding to the Lavezzi Island Reserve, which ispartially protected from spear and amateur fishing. This areais characterized by a predominantly rocky substrate andPosidonia oceanica seagrass beds at shallow depths. Area 1 isrocky and relatively deep, area 2 consists of rocky and sandyhabitats, whereas area 3 is rocky at shallow depths nearest theshoreline and sandy within the Strait.

From May to September 2000, 188 fish landings wererandomly recorded upon the return of the fishers from thefour fishing areas. In this paper, we term the locations wherefishing took place in each area ‘sites’. We add that 90% of thefishers were fishing in several areas on different trips so therewas no bias associated with particular individuals fishingrepeatedly in particular areas. The time period involvedcorresponded to the warm season (water temperature�20°C). All of the species caught were measured and theirtotal weight estimated using size-class correspondences(Bauchot & Pras 1980). The fishing area, the duration, thenets that were deployed and the fishing effort in terms of netsize were recorded. To standardize the fishing data collected,capture per unit effort (CPUE) was estimated, expressed ingrammes per net unit length of 50 m per 24 h. Fifty-fourspecies were sampled and included fishes, cephalopods andcrustaceans.

386 D. Mouillot et al.

Area 4Area 2 Area 3Area 1

Serranus scriba *

Labrus merula *

Spondyliosoma cantharus *

Epinephelus marginatus *

Scorpaena porcus *

Scorpaena scrofa *

Sciaena umbra

Symphodus tinca *

Diplodus puntazzo *

M. helena *

Diplodus annularis*

Diplodus vulgaris *Bothidea/ Soleidea

H. gamarus

Palinurus elephasMaja verrucosa

M. merluccius *

S. canicula

D. sargus *

N

E

S

WCorsica island

N.W.Mediterranean

Bonifacio Strait

10 km

Figure 1 Bonifacio StraitMarine Reserve (Corsica,France) with the four areasindicated. Species for which theindicator value was significant(5% level) are shown in thecorresponding area. * � specieswith a significant higher CPUEbiomass with the Tukey aposteriori test (after classicalANOVA).

Indicator species analysis

In this study, the IndVal combined a species’ relative abun-dance (specificity) with its relative frequency of occurrence ina group of sites (fidelity), referred to as an ‘area’. These twocomponents were included because an indicator species thatis defined as the most characteristic of one area must be foundmostly in this area and be present in the majority of the sitesbelonging to that area. For example, if one species is capturedwith high biomass in a single site of one area and only in thisarea, we can conclude that the species has a high specificityfor this area but it cannot be considered as an indicatorspecies. Alternatively, if one species is present in all sites ofone area but with a low relative abundance, we can concludeit has a high fidelity for this area but it cannot be consideredas an indicator species.

To take into account this duality, the IndVal values werecalculated for each species j and for each area k according tothe formula (Dufrêne & Legendre 1997):

IndValkj � 100 � Akj � Bkj

where Akj is a measure of specificity and Bkj is a measure offidelity according to the formulae:

Akj � Nindividualskj / Nindividuals�j

and

Bkj � Nsiteskj / Nsitesk�

In our case, Nindividualskj is the mean mass CPUE of speciesj for the sites examined in area k (areas in the present case aredefined in Fig. 1 and sites are locations where nets aredeployed within an area). Nindividuals

�j is the sum of themean mass CPUE of species j within the various areas k.Nsiteskj is the number of fished sites in area k where species jis present and Nsitesk�

is the total number of fished sites inthat area (Dufrêne & Legendre 1997; Legendre & Legendre1998; McGeoch & Chown 1998).

The indicator value of species j for an area k (IndValkj) ismaximum (100%) when all the individuals of species j areobserved in fish landing records (fished sites) belonging to asingle area. The largest value of IndValkj observed over thefour areas for a species j is IndValj � Max(IndValkj). Sites arerandomly permuted between areas to obtain a distribution forIndValj , significance being evaluated by the differencebetween the observed value and the proportion of values largerfor the simulated set of data (Dufrêne & Legendre 1997). Thecalculations of IndVal values and the associated tests wereperformed using the PC-ORD 4.0 for Windows software(McCune & Mefford 1999); 10 000 iterations were performed.

ANOVA-like tests

An analysis of variance, followed by an a posteriori Tukey test(Zar 1984) was carried out on the same data to identify those

species possessing a higher CPUE in a given area.Interpretation of ANOVA results based on data obtained byfishing may be incorrect, however, as the assumptions ofthese tests are rarely met. Thus, the normality of the data foreach species and in each area was examined using thenormality test developed by Ryan and Joiner (1976), which isbased on that of Shapiro and Wilk (1965), the latter beingrecognized as robust in the detection of non-normality of data(Shapiro et al. 1968; Mardia 1980). The variance homo-geneity for each species between the four sites was also testedusing the method of Levene (1960) instead of the Bartletttest, as the latter is known to be very sensitive to non-normality in the distributions of samples (Box 1953; Manly1998). All these tests were applied on the log-transformedCPUE data to reduce non-normality in the distributions.Due to the violations of the conditions required to perform anANOVA test, differences between areas were also tested foreach species using the non-parametric Kruskal-Wallis test.Normality, variance homogeneity, ANOVA, Tukey andKruskal-Wallis tests were performed using the Minitab soft-ware, version 12.

A one-way randomization ANOVA was performed on thesame data. Manly (1998) has suggested randomization testsfor this type of data and demonstrated that the value of pobtained (Type I error) is considered to be the true value ofthe test. The conditions required to apply an ANOVA are notnecessary for this type of test, which has the advantage, ascompared to non-parametric tests, of taking into consider-ation individual values and not just their rank (Manly 1998).We therefore carried out a randomization ANOVA of the fishlanding records (10 000 iterations) between the four areas,using RT software, version 2.1 (Manly 1997).

RESULTS

Classical tests

Several statistical tests were performed on the same data set.At least 30 fish landing records were obtained per area. Whenwe log transformed CPUE data, at the 5% significance level,normality was rejected for half of the distributions examined(54 species and 4 areas). For 21% of the fished species, vari-ance homogeneity was rejected at the 5% significance level.

ANOVA and the a posteriori Tukey test distinguished 13species possessing a mass CPUE significantly higher for oneof the areas (Fig 1). It is apparent that the ANOVA andrandomization ANOVA results were similar (Table 1; 7.14%disagreement at the 1% significance level). The Kruskal-Wallis generated very different results from those of theANOVA tests (parametric and randomization; Table 1).

Indicator species test

Of the 54 species caught by fishers, randomization identified19 having a significant indicator value (5% level of signifi-cance) for one of the four areas (Fig. 1). Thus, for six

Indicator species analysis in MPAs 387

(19–13) species the IndVal value was significantly greater forone area, whereas the biomass was not significantlydifferent. We selected three species among the sixmentioned above which allowed the advantages of indicatorspecies analysis to be highlighted for three distinct scenarios(Table 2).

Scenario 1: abundant species with similar fidelity between areasbut high specificity for one area (Palinurus elephas)Based on the ANOVA and Tukey test results, the meanCPUE for Palinurus elephas did not significantly differbetween areas 1, 2 and 3, although this was an indicatorspecies of area 1 (Fig. 1). For this species, the fidelity values(B in Table 2) were almost identical between areas 1, 2 and 3,whereas specificity was much more elevated at area 1 (A inTable 2).

Scenario 2: species with high fidelity for one area (Sciaenaumbra)Although the mean CPUE for Sciaena umbra was higher inarea 4 (Table 2), the Tukey test failed to detect any differ-ences between areas (ANOVA, p � 0.2). This prevalence inarea 4 was expressed by the higher specificity observed there.The fidelity of S. umbra was even more pronounced at area 4,

relative to the other three areas. In fact, this species wasrarely captured there, which explains the relatively lowfidelity, but that the captures that did occur were of highbiomass (B in Table 2).

Scenario 3: rare species with high fidelity and specificity for onearea (Maja verrucosa)The last species examined was Maja verrucosa, for whichcatches using nets were rare (low CPUE in Table 2). Thus,although the mass CPUE recorded at area 4 was much higher(by a factor of 10) than those recorded at the other areas, byANOVA the difference was not significant (p � 0.15). Thefidelity and particularly the specificity of this species at area 4were very high, thus making it an indicator species for thisarea (p � 0.05).

DISCUSSION

The novelty of this approach is to include both occurrenceand abundance data of species, whereas classical tests onlyuse distribution of abundance in various areas, so it is notsurprising to find different results. In the Bonifacio StraitMarine Reserve, we wanted to identify non-random distrib-utions of species between areas and to formulate somehypothesis to explain these differences. The three scenariosdescribed in the results are discussed below.

In Scenario 1, indicator species tests were more capable ofdetecting differences between areas when abundances weredifferent, despite homogeneous fidelity values. Palinuruselephas was particularly well adapted to the habitat present atarea 1, which was composed of jagged and deep-lying rocks.

In Scenario 2, the fact that Sciaena umbra was detected asan indicator species of area 4 highlights importance of thefidelity parameter of a species, as it is this parameter thatresults in a significant IndVal value, even when a species’biomass does not differ significantly between areas (Table 2).It is important to note that S. umbra is known to be sensitiveto spear fishing and all of area 4 has been protected from thistype of activity since 1982. This information is not enough toinfer the impact of a marine reserve but it can constitute ahypothesis to explain this result.

In Scenario 3, Maja verrucosa was a rare species in theBonifacio Strait Marine Reserve (Table 2). This type of datais very common in traditional fisheries and these species arenever cited as being indicator species of a given area using theclassical tests. They often possess an intrinsic cultural orecological value, such as is seen for M. verrucosa. This specieswas detected as an indicator species of area 4 because of thefavourable habitat occurring there.

In traditional fisheries or visual census data non-normaldistributions and high variances are common. In the presentcase, despite the log-transformation of data, initial require-ments were not met for the ANOVA test. When wecompared results obtained from randomization and ANOVAtests (Table 1), ANOVA appeared to be particularly robustfor species data sampled in this study. The limited power of

388 D. Mouillot et al.

Table 1 Percentage of differences in the results obtained at 5%(D5%) and 1% (D1%) levels between classical and randomizationANOVA and Kruskal-Wallis tests. Results are different when onetest leads to a significant difference and the other test leads to nosignificant difference between two species’ biomasses. These resultswere obtained for the 54 species, each test was done on eachspecies.

Test ANOVA Kruskal-WallisKruskal-Wallis D5% 25.93%

D1% 12.96%Randomization D5% 14.28% 24.07%

D1% 7.14% 12.96%

Table 2 Abundance (mean CPUE), specificity (A) and fidelity (B)of three species for the four areas in the Bonifacio Strait MarineReserve.

Area1 2 3 4

Maja verrucosamean CPUE (SD) 0,9 (5,5) 1,0 (6,4) 0,2 (0,9) 5,2 (20,8)A (%) 12 3 14 71B (%) 7 2 7 19Palinurus elephasmean CPUE (SD) 132 (157) 83 (104) 84 (86) 37 (84)A (%) 39 25 25 11B (%) 81 81 79 34Sciaena umbramean CPUE (SD) 13 (73) 15 (39) 3 (8) 24 (56)A (%) 24 28 5 43B (%) 9 26 14 42

non-parametric tests does not fully explain the 25% differ-ence in the data (Table 1). Manly (1998) attributed the lackof sensitivity of non-parametric tests to tied zero scores. Inmarine biology, tied zero scores tend to be a substantialproportion of sample data, be they obtained by visual censusor traditional fisheries activities. This is clearly true in thepresent study, where 37 species were absent from at least70% of the 188 samples, and 16 species had zero biomass inmore than 90% of the samples.

This approach allows indicator species of a given area orenvironment to be identified even when those species arerather scarce (Maja verrucosa), exhibit similar abundancesbut a marked area fidelity (Sciaena umbra) or exhibit a homo-geneous fidelity between sites but a very marked specificityor abundance (Palinurus elephas). In the Bonifacio StraitsNatural Reserve this approach allowed us to identify 19species as being indicative of a particular area, which is animportant factor within the context of conservation andmarine reserve impact assessment studies. Even if theseresults are not sufficient to assess reserve impact, we canevoke some hypotheses. Area 4, which has been protectedfrom spear fishing and amateur fishing activities since 1982,is characterized by 13 significant indicator species (Fig. 1).Among these species, seven are targets of spear fishing,namely Diplodus vulgaris, Diplodus puntazzo, Serranus scriba,Symphodus tinca, Labrus merula, Sciaena umbra andEpinephelus marginatus. LaMesa and Vacchi (1999) foundhigher abundance of Serranus scriba and Epinephelusmarginatus, higher occurrences of Serranus scriba,Epinephelus marginatus and Labrus merula and higherpercentages of large individuals in Serranus scriba andSpondyliosoma cantharus in protected areas as compared tounprotected areas. This study supports a relationshipbetween indicator species and the protected area establishedin 1982. In a recent study on the human impact in theMediterranean littoral, Guidetti et al. (2002) recorded higherabundances of Symphodus tinca, Scorpaena porcus, Serranusscriba, Diplodus annularis, Diplodus puntazzo and Diplodusvulgaris in control locations than in locations impacted bysewage or date-mussel fisheries. Similarly, these species weredetected as significant indicators of area 4, the least human-impacted area in our study.

From a theoretical point of view, there are two ways toassess impact of protection in the Bonifacio Strait MarineReserve:

• BACI (before-after-control-impact) design to studyspecies assemblages before and after the protection, and

• comparison of assemblages between protected andcontrolled areas.

A BACI design is actually the best way to identify protec-tion impact because from a practical point of view it is verydifficult to have a controlled area which is exactly the same,in terms of habitat, environmental variables, flora and humaninfluences, as that protected in the marine environment. In

our study, the four areas were very close but habitats weredifferent. With a BACI design, indicator species can be auseful method to detect non-random distributions of speciesbetween samples before and after protection instead ofclassical tests.

In conclusion, to identify non-random distributions ofspecies in ecological conservation programmes we suggest theuse of the indicator species method as an alternative toclassical tests because this index :

• takes into account both relative abundance between areasand occurrence in each area,

• is able to detect significant differences for rare species, and• this method can be used with data which contain a high

proportion of tied zero scores, present non-normal distri-butions and exhibit a wide variability.

ACKNOWLEDGEMENTS

We thank Guy-François Frisoni, manager of the BonifacioStrait Marine Reserve and Roger Pantalacci and JérômePolverini, director and president of the ‘Office del’Environnement de la Corse’ for facilities and financialsupport. We also thank fishers of the Bonifacio Strait MarineReserve, Dominique Rigo and Valérie Orsoni for their helpin the sampling programme and Monique Johnson for thetranslation of the manuscript. Comments by M.G. Butlerand an anonymous reviewer improved this paper.

References

Badalamenti, F., Ramos, A.A., Voultsiadou, E., Lizaso, L.J.S.,Danna, G., Pipitone, C., Mas, J., Fernandez, J.A.R., Whitmarsh,D. & Riggio, S. (2000) Cultural and socio-economic impacts ofMediterranean marine protected areas. EnvironmentalConservation 27: 110–125.

Bauchot, M.L. & Pras, A (1980) Guide des Poissons Marins d’Europe.Lausanne, Switzerland: Delachaux et Niestlé.

Belbin, L. & McDonald, C. (1993) Comparing three classificationstrategies for use in ecology. Journal of Vegetation Science 4:341–348.

Box, G.E.P. (1953) Non-normality and tests on variances.Biometrika 40: 318–335.

Chapman, M.R. & Kramer, D.L. (1999) Gradients in coral reef fishdensity and size across the Barbados Marine Reserve boundary:effects of reserve protection and habitat characteristics. MarineEcology Progress Series 181: 81–96.

Chytry, M., Tichy, L., Holt, J. & Botta-Dukat, Z. (2002)Determination of diagnostic species with statistical fidelitymeasures. Journal of Vegetation Science 13: 79–90.

Clarke, K.R. (1993) Non-parametric multivariate analyses ofchanges in community structure. Australian Journal of Ecology 18:117–143.

Culioli, J.M. (1994) La pêche professionnelle dans la RéserveNaturelle des Iles Lavezzi (Corse). Effort et productions.Travaux Scientifiques du Parc Naturel Régional de Corse 52:1–106.

Detsis, V., Diamantopoulos, J. & Kosmas, C. (2000) Collembolan

Indicator species analysis in MPAs 389

assemblages in Lesvos, Greece. Effects of differences invegetation and precipitation. Acta Oecologica 21: 149–159.

Dufrêne, M. & Legendre, P. (1997) Species assemblages and indi-cator species: The need for a flexible asymmetrical approach.Ecological Monographs 67: 345–366

Guidetti, P., Fanelli, G., Fraschetti, S., Terlizzi, A. & Boero, F.(2002) Coastal fish indicate human-induced changes in theMediterranean littoral. Marine Environmental Research 53: 77–94.

Hill, M.O. (1979) TWINSPAN: a Fortran Program for ArrangingMultivariate Data in an Ordered Two-way Table by Classification ofthe Individuals and Attributes. New York, USA: CornellUniversity.

Hufnagel, L., Bakonyi, G. & Vasarhelyi, T. (1999) New approachfor habitat characterization based on species lists of aquatic andsemiaquatic bugs. Environmental Monitoring and Assessment 58:305–316.

Jennings, S. (2001) Patterns and prediction of population recoveryin marine reserves. Reviews in Fish Biology and Fisheries 10:209–231.

LaMesa, G. & Vacchi, M. (1999) An analysis of the coastal fishassemblage of the Ustica Island marine reserve (MediterraneanSea). Marine Ecology: PSZN 20: 147–165.

Legendre, P. & Legendre, L. (1998) Numerical Ecology.Amsterdam, the Netherlands: Elsevier.

Levene, H. (1960) Robust tests for equality of variance. In:Contributions to Probability and Statistics, ed. I. Olkin, S.G.Ghurye, W. Hoeffding, W.G. Madow & H.B. Mann, pp.278–292. Stanford, USA: Stanford University Press.

Manly, B.F.J. (1997) RT, A Program for Randomization Testing,Version 2.1. Centre for Applications of Statistics andMathematics.

Manly, B.F.J. (1998) Randomization, Bootstrap and Monte CarloMethods in Biology. London, UK: Chapman & Hall.

Mardia, K.V. (1980) Tests for univariate and multivariatenormality. In: Handbook of Statistics, ed. P.R. Krishnaiah, pp.279–320. Amsterdam, the Netherlands: Elsevier.

McClanahan, T.R. & Arthur, R. (2001) The effect of marinereserves and habitat on populations of east African coral reeffishes. Ecological Applications 11: 559–569.

McClanahan, T.R. & Kaunda-Arara, B. (1996) Fishery recovery ina coral-reef marine park and its effect on the adjacent fishery.Conservation Biology 10: 1187–1199.

McCune, B. & Mefford, M.J. (1999) PC-ORD. MultivariateAnalysis of Ecological Data, Version 4. Gleneden Beach, Oregon,USA: MjM Software Design.

McGeoch, M.A. & Chown, S.L. (1998) Scaling up the value ofbioindicators. Trends in Ecology & Evolution 13: 46–47.

Mikusinski, G., Gromadzki, M. & Chylarecki, P. (2001)Woodpeckers as indicators of forest bird diversity. ConservationBiology 15: 208–217.

Mouillot, D. & Culioli, M. (2002) The sample size necessary toassess changes in fish biomass: a reply. Marine Ecology: PSZN 23:11–18.

Mouillot, D., Culioli, J.M., Leprêtre, A. & Tomasini, J.A. (1999)Dispersion statistics and sample size estimates for three fishspecies (Symphodus ocellatus, Serranus scriba and Diplodus annu-laris) in the Lavezzi Islands Marine Reserve (South Corsica,Mediterranean Sea). Marine Ecology: PSZN 20: 19–34.

Pipitone, C., Badalamenti, F., Danna, G. & Patti, B. (2000) Fishbiomass increase after a four-year trawl ban in the Gulf ofCastellammare (NW Sicily, Mediterranean Sea). FisheriesResearch 48: 23–30.

Rakitin, A. & Kramer, D.L. (1996) Effect of a marine reserve on thedistribution of coral reef fishes in Barbados. Marine EcologyProgress Series 131: 97–113.

Renjifo, L.M. (2001) Effect of natural and anthropogenic landscapematrices on the abundance of sub-Andean bird species. EcologicalApplications 11: 14–31.

Rice, J.C. (2000) Evaluating fishery impacts using metrics ofcommunity structure. Ices Journal of Marine Science 57: 682–688.

Ryan, T.A. & Joiner, B.L. (1976) Normal probability plots and testsfor normality. Technical Report, Statistics Department, ThePennsylvania State University, PA, USA.

Shapiro, S.S. & Wilk, M.B. (1965) An analysis of variance test fornormality (complete samples). Biometrika 52: 591–611.

Shapiro, S.S., Wilk, M.B. & Chen, H.J. (1968) A comparative studyof various tests for normality. Journal of American StatisticalAssociation 63: 1343–1372.

Stork, N.E. & Samways, M.J. (1995) Inventorying and monitoringof biodiversity. In: Global Biodiversity Assessment, ed. V.H.Heywood, pp. 453–543. Cambridge, UK: Cambridge UniversityPress.

Tratalos, J.A. & Austin, T.J. (2001) Impacts of recreational SCUBAdiving on coral communities of the Caribbean island of GrandCayman. Biological Conservation 102: 67–75.

Zar, J.H. (1984) Biostatistical Analysis. Englewood Cliffs, USA:Prentice-Hall International Editions.

Zimmer, K.D., Hanson, M.A. & Butler, M.G. (2000) Factors influ-encing invertebrate communities in prairie wetlands: amultivariate approach. Canadian Journal of Fisheries and AquaticSciences 57: 76–85.

390 D. Mouillot et al.