Ga

MI

a

ARRA

KBMGF

1

st(erdcedmee

fa22iafi

1h

Ecological Indicators 29 (2013) 159–166

Contents lists available at SciVerse ScienceDirect

Ecological Indicators

jo ur nal homep age: www.elsev ier .com/ locate /eco l ind

eometric morphology as an alternative for measuring the diversity of fishssemblages

arc Farré, Victor M. Tuset ∗, Francesc Maynou, Laura Recasens, Antoni Lombartenstitut de Ciències del Mar (CSIC), Passeig Marítim de la Barceloneta 37-49, 08003 Barcelona, Catalonia, Spain

r t i c l e i n f o

rticle history:eceived 27 September 2012eceived in revised form 4 December 2012ccepted 6 December 2012

eywords:iodiversityorphological diversity

a b s t r a c t

Biological diversity can be measured using ecological, taxonomic and functional indices, although numer-ous studies have concluded that organism morphology can be also a source for computing diversityindices. In the present study, we characterized fish morphology using geometric morphology, whichincluded body shape landmarks as well as the morphology of fins, and we computed the correlationamong ecological, taxonomic, functional and morphological indices (including a new index defined herecalled “morphological richness”). Morphological indices were calculated both from abundance data andpresence/absence data. To carry out this study, thirteen fish assemblages of two different areas of the Cata-

eometrical morphologyish assemblages

lan coast (north-western Mediterranean) were analyzed. The data was sampled by commercial fishingvessels using trammel nets. The results clearly indicated that each type of morphological index is relatedto one dimension of the biodiversity space. Furthermore, we demonstrated that presence/absence dataprovided the similar results as abundance data when using morphological indices, opening the possibilityto analyze the evolution of fish assemblages over time from species checklists collected in experimental

cent

surveys from the late XIX

. Introduction

A recent study organized biodiversity in a three-dimensionalpace formed by one dimension of structural complexity, andwo different mixtures of taxonomic and functional diversityLyashevska and Farnsworth, 2012). Functional diversity is consid-red as the main key in biodiversity studies because it explains theoles that organisms play within ecosystems. Measuring functionaliversity is usually approached by pooling functional traits whichan be any biological feature that affects species (i.e. prey capturefficiency, diet, foraging methods, size, mode of locomotion, repro-uction or habitat) or which are a combination of morphologicaleasures with ecological meaning and functional role (Mouillot

t al., 2005; Somerfield et al., 2008; Villéger et al., 2010; Albouyt al., 2011).

In the study of fish assemblages, the most used morpho-unctional characters are mainly focused on food acquisitionnd locomotion (Webb, 1984; Fulton et al., 2001; Dumay et al.,004; Bellwood et al., 2006; Villéger et al., 2011; Albouy et al.,011). However, there are some other morphological character-

stics that are not usually considered and give relevant informationbout fish adaptations to environment. For example, tripod-shes (Bathypterois spp.) have elongate pelvic and caudal fin rays

∗ Corresponding author. Tel.: +34 932 309 500; fax: +34 932 309 555.E-mail address: vtuset@icm.csic.es (V.M. Tuset).

470-160X/$ – see front matter © 2012 Elsevier Ltd. All rights reserved.ttp://dx.doi.org/10.1016/j.ecolind.2012.12.005

ury to the present.© 2012 Elsevier Ltd. All rights reserved.

that hold the body above the sediment, resulting in so-called‘bathypteroiform’ movement, which describes the functional roleof the fins associated with landing (Davis and Chakrabarty, 2011); insome scorpaeniform fishes the paired fins (pelvic and pectoral fins)have other functions associated with a life style on the sea, suchas “walking”, perching or probing for food items (Gosline, 1994);suckerfishes (Echeinidae) are commensals of sharks and rays whoattach to their hosts by means of a powerful suction disc that is atransformation of the dorsal fin (Muss and Nielsen, 1999); somegoatfishes (Mullidae), haddocks or cods (Gadidae) have sensorialbarbels on the chin, which allows them to discriminate and locatesources of sensory stimuli (food sources or predators) (Lombarteet al., 2003); ophidiids have pelvic fins modified as sensory bar-bels (Helfman et al., 1997); and in tetraodontiforms four differentlocomotion patterns are described, related to differentiated posi-tion and shape of dorsal, anal and pectoral fins (Colgate and Lynch,2004). In addition, the eye diameter is a key factor in food acquisi-tion, although in complex habitats (such as coral reefs) or with highturbidity the visual field decreases favouring the development ofother sensorial organs (Lombarte and Aguirre, 1997; Willis et al.,2005).

The shape and position of morphological characters can alsobe described using geometric morphometry based on landmarks

analyses (Bookstein, 1991; Rohlf and Marcus, 2003). Initially,this was used to test for significant correlations between bodyshape and ecological traits (Langerhans et al., 2004; Clabaut et al.,2007; Lombarte et al., 2010). Later, this technique was used to

160 M. Farré et al. / Ecological Indic

mMcoootptf1adespetlaclfiTmt

craie

2

2

olpu2sia

2.3. Geometric morphological indices

A total of 27 landmarks and semilandmarks (Fig. 2) withanatomical, ecological and taxonomical meaning were used

Fig. 1. Geographical localization of sampling.

easure the variation of biodiversity in time or space (Neige, 2003;cClain et al., 2004). In addition, the spatial distribution of fishes,

alled morphospace, allows discerning the structural complexityf communities (Willis et al., 2005). Although there are numer-us metrics in the literature, morphological disparity is a measuref the amount of morphological variation in a group of samples,aking into account the volume of the hyperdimensional mor-hospace occupied, the relative distances between samples, andhe number of samples (Clabaut et al., 2007); and it is often usedor quantifying the species distribution within morphospace (Foote,993; Ciampaglio et al., 2001). Thus, morphological disparity isn invaluable source of information enriching the knowledge ofynamic processes of ecological systems (Roy et al., 2001; McClaint al., 2004; Gerber et al., 2008). Even more, the organism shape istrongly related to taxonomy, and the taxonomy can be used as aroxy for the phylogenetic affiliations (Zelditch et al., 2003; Pricet al., 2011). In fishes, the position and number of body fins is aaxonomical criterion (Nelson, 2006), and its identification withandmarks is very easy. Thus, Recasens et al. (2006) developed

geometric morphological index for fishes including the generalharacterization of body shape, head, eye, mouth and position andength of fins to provide a comprehensive picture of the structure ofsh assemblages, including ecological and evolutionary processes.he goal was to create an index easily acquired providing infor-ation on diversity and structure fish assemblages at the same

ime.Herein, (i) we establish a new geometric morphological index

alled morphological richness (MR), (ii) we compare morphologicalichness and other geometric morphological indices among them,nd (iii) we evaluate the usefulness of the geometric morphologicalndices in studies of fish assemblages analysing their correlation tocological, taxonomical and functional diversity indices.

. Materials and methods

.1. Sampling

This study used data from shallow-water fish assemblagesff the Catalan coast, western Mediterranean Sea (Fig. 1), col-ected with commercial fishing gear during different researchrojects. A total of 51 samples were obtained by small-scale vesselssing trammel nets between February 2000–April 2001, December

002–September 2003, and May 2009–April 2010 in the centraltudy area (Vilanova i la Geltrú-Calafell). Additionally, 35 fish-ng operations by seven trammel netters in the northern studyrea (L’Estartit) were analyzed between March and December

ators 29 (2013) 159–166

2003–2005. The characteristics of trammel net fisheries in the studyareas are described elsewhere (Recasens et al., 2006; Stelzenmülleret al., 2009; Maynou et al., 2011; Martín et al., 2012).

For each fishing operation the entire fish catch (commercial plusdiscarded fraction) was retained. Species were identified (Mercaderet al., 2001) and placed within a taxonomic hierarchy accordingto Nelson’s Fishes of the World (Nelson, 2006). Specimens werephotographed using standardized body position (Recasens et al.,2006).

In each locality, the habitat of each fishing operation wasclassified according to the characteristics of the sea bottom.Seven types were established in the north of the Catalan coast:(a) Sandy, <10 m depth; (b) Sandy, 10–20 m; (c) Sandy-Rocky,>20 m; (d) Sandy-Muddy, 26–50 m; (e) Muddy, 30–33 m; (f) Rocky-Sandy, 10–22 m; and (g) Gravel-Sandy, 25–32 m. Six types ofbottom were recognized in the central Catalan coast: (a) Sandy,<10 m of depth; (b) Sandy, >20 m; (c) Sandy-Rocky, 15–19 m; (d)Rocky-Sandy, 15–19 m; (e) Artificial reef-Sandy, 15–19 m; and (f)Rocky-Seagrasses, 10–14 m.

2.2. Structural, taxonomical and functional indices

Several structural or ecological indices were calculated in orderto characterize the species assemblages in each habitat type for thenorth and central locations: number of species or species richness(S), Margalef’s richness index (d) (Margalef, 1958), Shannon’s diver-sity index (H′) (Shannon and Weaver, 1949), Simpson index (S′)(Simpson, 1949), and Pielou’s evenness index (J′) (Pielou, 1975).Four taxonomic diversity indices were also considered (Warwickand Clarke, 1995; Clarke and Warwick, 1998, 2001): taxonomicdiversity (�), taxonomic distinctness (�*), average taxonomic dis-tinctness AvTD (�+) and variation in taxonomic distinctness VarTD(�+).

Functional diversity (FD) was calculated using the followingfunctional traits (Table 1) (Colgate and Lynch, 2004; Somerfieldet al., 2008; Villéger et al., 2010). It was calculated using the fol-lowing protocol: (i) construction of a species–trait matrix; (ii)conversion of species–trait matrix into a distance matrix; (iii) clus-tering distance matrix into a dendrogram using UPGMA; and (iv)calculating functional diversity by summing dendrogram branchlengths of community species (Petchey and Gaston, 2006; Pavoineet al., 2009). Since the number of categories assigned to a traitmay influence the specific weight of this category in the subse-quent analysis (e.g., body shape has up to eight categories, whileothers have only two), the procedure was offset by including func-tional variables that had the same or similar weight (trophic level,resilience or growth rate) (Somerfield et al., 2008). Biological data todefine these functional traits were obtained from published studiesand Fishbase (Froese and Pauly, 2011).

Fig. 2. Identification of 27 landmarks and semilandmarks used in the geometricmorphological analysis.

M. Farré et al. / Ecological Indicators 29 (2013) 159–166 161

Table 1Functional traits used to characterize the functional diversity of each fish assemblage (Colgate and Lynch, 2004; Somerfield et al., 2008; Froese and Pauly, 2011).

Traits Categories Trait Categories

Body shape Anguilliform Habitat DemersalFusiform PelagicElongatedOblong Environment Exclusively marineOval Other environmentsSymmetrical flatfishAsymmetrical flatfish Life span Very low (minimum 14 years)

Low (4.5–14 years)Special fins Presence Medium (1.4–4.5 years)

Absence High (maximum 1.5 years)Sensorial barbels Presence Maximum length >10 cm

Absence >20 cm>40 cm

Buying ability Yes >80 cmNo

Growth rate >0.1 year−1

Motility Sedentary >0.2 year−1

Mobile >0.3 year−1

>0.4 year−1

Locomotion Anguilliform >0.5 year−1

Subcarangiform >0.7 year−1

CarangiformThunniform Trophic level <2.5Amiiform 2.6–3.0Balistiform 3.1–3.4Tetraodontiform 3.5–4.0Bathypteroiform 4.1–4.5

(f2wae(t(fiMdm2tt

i

sAwsmducpcit2ript

Spawning period Short (<3 months)Large (≥3 months)

Recasens et al., 2006; Tables 2 and 3) for a specimen of each speciesrom standardized images of the left side (using tpsDig, 2.16; Rohlf,001). After digitalising the metric maps of each species, theyere rotated, scaled (to unit centroid size) and translated using

generalized least-square superimposition (GLS) procedure (gen-ralized Procrustes) to remove scale and orientation distortionsusing tpsRel 1.24; Rohlf, 2001). A thin-plate spline representa-ion was used to fit an interpolated function to an average mapconsensus configuration) of the profile shape and derive the uni-orm (relative warp) components of the shape variation. Changesn shape were visualized using relative warp analysis (Rohlf and

arcus, 2003). The first eight relative warp scores were selected toescribe each species as they explained more than 98% of the totalorphological variability (Recasens et al., 2006; Lombarte et al.,

012). The morphospace of each fish assemblage was defined usinghe area inside the convex hull (Cornwell et al., 2006) enclosing allhe species among warps 1, 2 and 3.

Relative warps were used to calculate morphological dispar-ty, MD =

∑jRW

2

j/(N − 1), where RWj are the relative warps of

pecies j and N is the total number of species (Zelditch et al., 2003;ntonucci et al., 2009). Furthermore, two indices were obtainedith a similar protocol to that used to compute functional diver-

ity (Petchey and Gaston, 2006): (i) construction of a species–traitatrix; (ii) conversion of species–trait matrix into an Euclidean

istance matrix; (iii) clustering distance matrix into a dendrogramsing UPGMA; and (iv) summing dendrogram branch lengths ofommunity species. From these data we calculated the new mor-hological richness index, MR =

∑jCC, where CC is the cluster

oefficient and j is the species, and the morpho-geometric diversityndex, EMI =

∑jCC/(N − 1), where CC is the cluster coefficient, j is

he species and N is the total number of species (Lombarte et al.,012). The trees were subject to 1000 nonparametric bootstrapeplicates to assess branch support. Finally, each morphological

ndex was calculated from abundance data (MD, MR and EMI) andresence/absence data (MD2, MR2 and EMI2). In the first case,he number of images analyzed per species was equivalent to the

>4.5

relative abundance in the samples. The species with abundanceequal to or less than 1% were analyzed as one image, so as to includethe largest possible number of species in the analyses. Indepen-dently of analysis type, in the presence/absence case, each specieswas represented by only one image (Recasens et al., 2006; Lombarteet al., 2012).

We selected two fish assemblages, sandy <10 m (north coast)and Rocky-Seagrasses (central coast), as example to explain thecorrelation among the morphological indices and the structure ofassemblages. The criteria for the selection of these fish assemblageswere based on dissimilarity in the morphological disparity, spe-cific richness and functional diversity (see results) considering thebest examples to explain the proposed targets. In addition, mor-phospaces were graphed to show the position of species usinggeometric morphological analysis. They were defined using theconvex hull enclosing all the species from each fish assemblages(Cornwell et al., 2006).

2.4. Comparison of indices

The comparison of diversity indices was performed usingSpearman’s correlation index and they were clustered in a den-drogram produced using the hierarchical method (Lyashevska andFarnsworth, 2012). It was carried out based on Euclidean distances,using Ward’s aggregation algorithm. Previously, data were scaledusing Z-score normalization because it is specially needed for dis-tance metrics, such as Euclidean distance, which are sensitive todifferences in the magnitude or scales of the attributes (Visalakshiand Thangavel, 2009).

3. Results

3.1. Meaning of geometric morphological indices

The two selected fish assemblages (sandy <10 m and Rocky-Seagrasses) showed a high variability in structure, specificcomposition, specific richness, and dominance. The sandy fish

162 M. Farré et al. / Ecological Indicators 29 (2013) 159–166

Table 2List of the 27 landmarks and semilandmarks used for explaining morphology of the body shape.

Points Morphological meaning Importance

1–2 Position and relative size of the mouth length respect to head and body size (from Jenkins andBurkhead, 1993)

Ecological meaning: related to food acquisition

3 Differentiates among fishes with or without sensorial barbels on chin, and position and relative sizeof barbels (from Kasumyan, 2011)

Ecological meaning: extremely diverse, relatedto mode of life, behaviour and feedingstrategies. Taxonomic value

4–5 Position and relative size of eye and its position respect to head and body size Ecological meaning: related to food acquisitionand life strategies

6–8 Indicates the relative height and width of head respect to the body size Ecological meaning: related to food acquisitionand life strategies

assbMwSw(

amvov(vhi(

ot(SOatca

24 Indicates the presence/absence of one or two dorsal fins

27 Defines the point of maximum height of the body shape

ssemblage was composed by 26 species of 16 families, with fourpecies (Solea solea, Sarpa salpa, Mugil cephalus and Chelon labro-us) comprising 60% of total catch in number, whilst most specieselonged to three families, Sparidae (32%), Soleidae (27%) andugilidae (19%). By contrast, Rocky-Seagrasses fish assemblageas represented by 43 species of 18 families. Mullus surmuletus,

corpaena notata and Pagellus acarne provided 60.7% of catch, whichas dominated by three families, Mullidae (39%), Scorpaenidae

21%) and Sparidae (20%).These differences in the specific composition of both fish

ssemblages were showed in the values obtained of geometricorphological indices. The sandy sample showed greater relative

ariability of taxonomical groups, taking into account the numberf species, attaining a higher morphological disparity (MD = 0.054s. 0.025) and morpho-geometric diversity (EMI = 0.19 vs. 0.16)Table 4). Nevertheless, morphological richness attained higheralues in Rocky-Seagrasses sample (MR2 = 6.98 vs. 4.75), whoseabitat favoured variety of life strategies which was reflected

n an increase of the functional diversity (FD = 101.80 vs. 67.50)Table 4).

The mosphospace was determined by the peripheral morphol-gy of fishes of each assemblage (Fig. 3). In the sandy sample,he morphology of these species was composed for flattenedPleuronectiforms as Bothus podas, Psetta maxima, S. solea andcophtalmus rhombus) and elongated shapes (Ophidiiforms asphidion rochei; Perciforms as Trachinus draco; and Syngnathiforms

s Syngnathus acus), who bury in the sand or live on the sea bot-om. Conversely, the more extreme shapes in the Rocky-Seagrassesorresponded with species living closer to surface (Clupeiformss Sardinella aurita; and Beloniforms as Belone belone), typical of

Taxonomic valueEcological meaning: related to locomotion andlife strategies

rocky-bottoms (Perciforms as Diplodus annularis, Symphodus rostra-tus and Scorpaena scrofa), and in lesser grade some flattened fishes(Pleuronectiforms as Arnoglossus thori and S. rhombus). The extremeshapes largely determined the space occupied for the remainingspecies, resulting in a high overlap between both communities.Thus, morphological disparity (MD) represented better the mor-phospace to maximize the distance among species; whilst, thehigher dispersion of species in the space favoured the increase ofmorpho-geometric diversity (EMI) and the morphological richness(MR) is associated to specific richness.

The interpolation of these results to whole data reinforced theidea that each geometric morphological index provides differentinformation because they were not correlated among them. Inaddition, it is interesting to highlight that similar results wereobtained using quantitative or qualitative (presence/absence) data(Table 5).

3.2. Comparison of diversity indices

The analysis of Spearman’s correlation showed a high and posi-tive relationship between richness indices, morphological richnessand functional diversity (Table 5). Moreover, Margalef’ richnessindex (d) was also correlated to Shannon (H′) and Simpson (S′)indices although to a lesser degree; whilst specific richness (S) wassignificant correlation to d (rs = 0.821) and H′ (rs = 0.601) indices.The remaining ecological indices were correlated among them and

with some taxonomical indices as taxonomic diversity (�) and tax-onomic distinctness (�*). Similarly to richness index, the Shannondiversity index was linked to morphological richness and func-tional diversity indices. Conversely, Pielou’s evenness index (J′)

M. Farré et al. / Ecological Indicators 29 (2013) 159–166 163

Table 3List of the 27 landmarks and semilandmarks used for explaining the fish morphology of fins.

Points Morphological meaning Importance

9–11 Indicates pectoral fin shape and its relative position (from Kasumyan, 2011) Ecological meaning: related to swimming orwalking over bottom, feeding and defensivestrategies. Taxonomic value

12–13 Indicates pelvic fin shape and its relative position (see scheme before) Ecological meaning: related to stability whileswimming, crawling, walking over bottom ortransformed in a suction cup type structuremaintaining position in fast moving current

14–16 Indicates anal fin shape and its relative position (see scheme before) Ecological meaning: related to stability whileswimming, walking over bottom and primarypropulsion in some species. Taxonomic value

22–23 Defines where second dorsal fin finishes and its relative position (see scheme before) Ecological meaning: related to stability whileswimming and life strategies. Taxonomic value

25–26 Defines where first dorsal fin starts and its relative position (see scheme before) Ecological meaning: related to stability whileswimming, primary propulsion in somespecies, large fins as strucutres for thedetection food or attracting preys ortransformed in a suction cup type structuremaintaining position. Taxonomic value

17–21 Defines shape of peduncle and caudal fin (from Bugas et al., 2009) Ecological meaning: related to locomotion

w(ocpdrwFtw0

3

dmg

as weekly associated to that morpho-geometric diversity indexEMI) (rs = 0.561) and morphological disparity (MD) (rs = 0.575)btained from abundance data. The taxonomical indices wereorrelated among them and with ecological and morphological dis-arity indices (MD and MD2), except for the variation in taxonomicistinctness (�+). Morpho-geometric diversity indices were cor-elated between them and with the two unrelated indices; EMIith Pielou’s evenness and EMI2 with the functional diversity.

inally, the functional diversity (FD) showed significant relationo some ecological and morphological richness indices, especiallyith specific richness (0.991) and morphological richness (MR2,

.962) (Table 5).

.3. Morphology as biodiversity metric

The hierarchical clustering and index correlations allowed toifferentiate three levels of diversity based on the geometricorphological indices (Fig. 4): (a) a morpho-functional diversity

roup (MR, MR2, d, S and FD), where morphology richness and

functional diversity were related together and with specific rich-ness; (b) a morpho-structural diversity group (EMI, EMI2 andVarTD), that measured the morphological and taxonomical distinct-ness of fishes within assemblages; and (c) a morpho-taxonomicaldiversity group (MD, MD2, AvTD, DELT, DSTR), where the morpho-logical disparity depended on taxonomical groups.

4. Discussion

The results from this study confirmed that geometric morpho-logical indices could be a useful tool to measure the diversity offish assemblages. This agrees with findings from studies on actualfish and gastropods assemblage structure (McClain et al., 2004;Lombarte et al., 2012).

The species richness (S) is an inadequate measure missing 88.6%

of total biodiversity (Lyashevska and Farnsworth, 2012), althoughit is, by far, the most common measure of biodiversity used by sci-entists, conservationists, and policy makers (Magurran, 1988; Gray,2000; Wilsey et al., 2005; Flynn et al., 2009). Most studies reveal a

164 M. Farré et al. / Ecological Indicators 29 (2013) 159–166

Table 4Ecological, taxonomic, morphological and functional indices obtained for the localities considered. d, Margalef’s richness index; H′ , Shannon’s diversity index; S, specificrichness; S′ , Simpson index; J′ , Pielou’s evenness index; �, taxonomic diversity; �*, taxonomic distinctness; �+, average taxonomic distinctness; �+, variation in taxonomicdistinctness; EMI and EMI2, morpho-geometric diversity; MD and MD2, morphological disparity; MR and MR2, morphological richness; FD, functional diversity.

Locality fish assemblages Ecological Taxonomic Morphological Functional

S d J′ H′ S′ � �* �+ �+ EMI MD MR EMI2 MD2 MR2 FD

North Gravel-Sandy 25–50 m 22 4.15 0.68 2.11 0.81 67.04 82.41 75.11 462.65 0.153 0.026 2.91 0.152 0.028 3.19 52.10Rocky-Sandy 10–25 m 16 3.66 0.72 2.01 0.79 60.82 77.17 84.17 478.47 0.197 0.038 2.76 0.206 0.037 3.09 41.50Sandy 10 ma 26 6.33 0.90 2.92 0.94 86.52 91.93 88.23 317.25 0.193 0.054 4.64 0.190 0.050 4.75 67.60Sandy 10–20 m 21 4.71 0.80 2.44 0.88 71.51 81.54 85.36 413.56 0.192 0.044 3.84 0.204 0.044 4.08 53.00Sandy-Rocky 20 m 25 5.31 0.64 2.09 0.78 58.48 75.16 85.39 336.39 0.183 0.040 4.21 0.173 0.037 4.15 64.40Sandy-Muddy 26–50 m 22 5.26 0.87 2.68 0.92 84.77 91.76 89.72 232.50 0.201 0.042 4.22 0.206 0.041 4.33 61.50Muddy 30–35 m 18 4.06 0.74 2.13 0.82 61.89 75.13 81.21 312.75 0.184 0.035 3.13 0.193 0.033 3.28 47.90

Central Sandy 10 m 30 6.04 0.77 2.62 0.88 69.17 78.57 82.64 330.94 0.178 0.034 5.16 0.160 0.032 4.64 73.90Sandy-Rocky 15–19 m 40 8.33 0.84 3.11 0.94 73.52 78.45 84.20 324.84 0.162 0.029 4.68 0.169 0.031 6.60 96.40Sandy 20 m 27 6.79 0.92 3.02 0.95 79.93 83.90 85.26 339.96 0.209 0.045 5.83 0.182 0.043 6.59 68.90Rocky-Seagrass 10–14 ma 43 6.89 0.63 2.37 0.82 68.37 83.28 83.48 315.85 0.156 0.025 5.43 0.166 0.032 6.98 101.80Rocky-Sandy 10–25 m 37 5.96 0.82 2.92 0.93 81.91 88.53 87.82 316.86 0.177 0.046 4.41 0.171 0.107 5.99 84.20

88.30

of moi

csf2db(fssIdqt(nisrchw(

TSSEs

Reef-Sandy 15–19 m 48 6.69 0.74 2.78 0.89 78.56

a It indicates the fish assemblages selected to as example to graphical illustration

ndices.

lear relationship between specific richness and functional diver-ity (FD) independently of the method used for classifying species ofunctional groups (Micheli and Halpern, 2005; Stelzenmüller et al.,009; Pease et al., 2012; present study). Nevertheless, functionaliversity is the key to understand the structure of communitieseing its conservation vital for the maintenance of species diversityLyashevska and Farnsworth, 2012). The high species diversity isrequently associated to a fine-scale niche partitioning of resourcepecialists, where each species occupy a part of the ecologicalpace available (Wainwright and Richard, 1995; Ricklefs, 2010).n fact, species richness may not be a good proxy for functionaliversity because functionally unique species may be lost moreuickly than functionally redundant species and, therefore, func-ional diversity loss does not always parallel species richness lossHalpern and Floeter, 2008; Flynn et al., 2009). Morphological rich-ess is a relevant variable quantifying variation of shapes and

t is not necessarily linked to species richness. For example, theandy (10 m) fish assemblages of north presented similar specificichness and functional diversity that sandy (20 m) sample of the

entral region (Table 4). The morphological disparity (MD) wasigher in the north because the number of taxonomical groupsas higher and more extreme; however the morphological richness

MR) was higher in the central sample because the species are more

able 5pearman’s correlation among ecological, taxonomic, morphological and functional indiceimpson index; J′ , Pielou’s evenness index; �, taxonomic diversity; �*, taxonomic distincMI and EMI2, morpho-geometric diversity; MD and MD2, morphological disparity; MRignificantly correlated.

Indices Ecological Taxonomic

S d J′ H′ S′ � �* �

Ecological S 1d 0.821 1J′ 0.063 0.292 1H′ 0.601 0.726 0.815 1S′ 0.448 0.618 0.905 0.974 1

Taxonomic � 0.385 0.456 0.831 0.842 0.894 1�* 0.393 0.363 0.525 0.572 0.632 0.879 1�+ 0.237 0.231 0.473 0.443 0.458 0.665 0.�+ -0.311 -0.258 -0.182 -0.316 -0.302 -0.423 -0

Morphological EMI -0.505 -0.275 0.561 0.074 0.254 0.264 0.MD 0.061 0.055 0.575 0.435 0.505 0.632 0.MR 0.825 0.907 0.327 0.669 0.604 0.473 0.EMI2 -0.693 -0.468 0.344 -0.132 0.030 0.083 -0MD2 0.124 0.052 0.425 0.353 0.409 0.573 0.MR2 0.941 0.934 0.259 0.724 0.615 0.533 0.

Functional FD 0.991 0.901 0.113 0.630 0.498 0.423 0.

88.90 322.10 0.163 0.051 4.74 0.158 0.168 7.11 105.30

rphospaces, its relation to morphological indices and the meaning of morphological

dissimilar including pelagic, benthopelagic and benthic species.None of the ecological diversity indices measured such variabil-ity except the morphological richness index. Therefore, this indexdoes not infer the role of ecological mechanism in the diversifica-tion of evolutionary lineages as occurs in studies of ecomorphologytraits (Winemiller, 1991; Motta et al., 1995; Fulton et al., 2001;Wainwright et al., 2002; Wagner et al., 2009), but it is useful tocompare the diversity among fish assemblages.

The specific richness (S), or morphological richness (MR), isnot always linked to variations in the morphospace as conse-quence of an increment of the taxonomical diversity (Campbellet al., 2011) or extreme shapes (Heino et al., 2005; present study).By contrast, the morphological disparity (MD) is a diversity met-ric providing an invaluable source of information complementingtaxonomic approaches (McClain et al., 2004; Gerber et al., 2008;present study). If the number of species is increased next to mor-phospace centroid, then morphological disparity decrease; whilstif the species tend to be preferentially added to the marginsof morphospace, then morphological disparity and morphospace

occupied could increase (Roy and Foote, 1997; Neige, 2003). Thatis clearly illustrated in the two fish assemblages used as examplein our study. The morphological disparity (MD), morpho-geometricindex (EMI) and taxonomic diversity (�) were higher in sandy than

s. d, Margalef’s richness index; H′ , Shannon’s diversity index; S, specific richness; S′ ,tness; �+, average taxonomic distinctness; �+, variation in taxonomic distinctness;

and MR2, morphological richness; FD, functional diversity. In bold the variables

Morphological

+ �+ EMI MD MR EMI2 MD2 MR2

637 1.357 -0.324 1126 0.379 0.038 1571 0.797 -0.099 0.522 1407 0.192 -0.297 -0.088 0.154 1.058 0.297 -0.077 0.839 0.308 -0.410 1567 0.802 -0.168 0.441 0.953 0.135 0.299 1478 0.385 -0.481 -0.280 0.214 0.885 0.454 0.278 1401 0.308 -0.346 -0.451 0.115 0.846 -0.605 0.193 0.962

M. Farré et al. / Ecological Indic

FRa

Rsctf(daf

Fsa

ig. 3. Morphospaces of two fish assemblages selected, sand <10 m (pink) andocky-Seagrasses (green), using relative warps1–2 (a) and 1–3 (b). In blue thecronyms of fishes.

ocky-Seagrasses; however the richness (S), functional diver-ity (FD) and morphological richness (MR) were lower. In bothases, three families attained around 80% of abundance, buthey corresponded to three different orders (Mugilliforms, Perci-orms and Pleuronectiforms) in sandy sample instead one order

Perciforms) in the Rocky-Seagrasses assemblage. Morphologicalisparity increases because species have different life strategiesnd also represents distantly related lineages; by contrast, theunctional redundancy decreases because there are less specialist

ig. 4. Hierarchical clustering of biodiversity indices using Ward distance indicatingeveral clusters: (1) morpho-functional diversity; (2) morpho-structural diversity;nd (3) morpho-taxonomical diversity.

ators 29 (2013) 159–166 165

species which are more flexible ecologically to avoid competition(Schoener, 1974; Roy et al., 2001).

Several studies have demonstrated that changes within a com-munity, resulting from natural or anthropogenic perturbations, canbe measured using morphological and functional traits (Ernst etal., 2006; Mason et al., 2007; Olden et al., 2008; Flynn et al., 2009;Villéger et al., 2010). Lombarte et al. (2012) showed that morpho-geometric diversity (EMI) provided excellent results to explain thestructural changes occurring in two South African estuaries fishassemblages, being more sensitive to diversity changes that Shan-non (H′) and taxonomic indices. The morpho-geometric diversityestimates how many clusters of shapes are present in the mor-phospace, and this index increases when the species are distributedheterogeneously and the number of clusters is high. That is the rea-son why this index is the most related to structural complexity offish assemblages.

In conclusion, the geometric morphological indices are usefultools for comparing the diversity among fish assemblages whenecological information is absent or scarce, which is very com-mon: by studying the variation in species shapes we can gainunderstanding on the function of these species, even if completeinformation on habitat structure is lacking. In addition, the morpho-logical indices based on abundance and presence/absence providedsimilar results and were strongly correlated. This would open thepossibility to analyze the evolution of fish assemblages over timesimply from landmark analysis from species in historical checklistscollected in experimental surveys from the late XIX century to thepresent, facilitating the analysis of long-term trends in fish diversitychanges.

Acknowledgements

Data of this study were financed by project “ESCAL 1” (Ref.PCC30004/99), “ESCAL 2” (Ref. 02P30015) of the DG Fisheries of theGovernment of Catalonia, “CONFLICT” (Ref. CGL2008-00047) of theSpanish National Research Plans and “EMPAFISH” (E.U. DG MAREproject Ref. 006539). Marc Farré Foix acknowledges a pre-doctoralgrant from the Government of Andorra, ATC010-AND, academicyear: 2012–2013. Dr. Victor Tuset is a worker of CSIC within themodality “JAE-Postdoc” of Programme “Junta para la Ampliaciónde Estudios” co-funded by the European Social Foundation (FSE).

References

Albouy, C., Guilhaumon, F., Villéger, S., Mouchet, M., Mercier, L., Culioli, J.M.,Tomasini, J.A., Le Loc’h, F., Mouillot, D., 2011. Predicting trophic guild anddiet overlap from functional traits: statistics, opportunities, and limitations formarine ecology. Mar. Ecol. Prog. Ser. 436, 17–28.

Antonucci, F., Costa, C., Aguzzi, J., Cautadella, S., 2009. Ecomorphology of morpho-functional relationships in the family of Sparidae: a quantitative statisticapproach. J. Morphol. 270, 843–855.

Bellwood, D.R., Wainwright, P.C., Fulton, C.J., Hoey, A.S., 2006. Functional versatilitysupports coral reef biodiversity. Proc. R. Soc. B 273, 101–107.

Bookstein, F.L., 1991. Morphometric Tools for Landmark Data. Geometry and Biology.Cambridge University Press, New York.

Bugas Jr., P.E., Copeland, J.R., Murphy, B. (Eds.), 2009. Virginia Master Naturist Train-ing Manual. Virginia.

Campbell, N., Neat, F., Burns, F., Kunzlik, P., 2011. Species richness, taxonomic diver-sity, and taxonomic distinctness of the deep-water demersal fish community onthe Northeast Atlantic continental slope (ICES Subdivision VIa). ICES J. Mar. Sci.68, 365–376.

Ciampaglio, C.N., Kemp, M., Mcshea, D.W., 2001. Detecting changes in morphospaceoccupation patterns in the fossil record: characterization and analysis of meas-ures of disparity. Paleobiology 27, 695–715.

Clabaut, C., Bunje, P.M., Salzburger, W., Meyer, A., 2007. Geometric morphometricanalyses provide evidence for the adaptive character of the Tanganyikan cichlid

fish radiations. Evolution 61 (3), 560–578.

Clarke, K.R., Warwick, R.M., 1998. A taxonomic distinctness index and its statisticalproperties. J. Appl. Ecol. 35, 523–531.

Clarke, K.R., Warwick, R.M., 2001. A further biodiversity index applicable to specieslists, variation in taxonomic distinctness. Mar. Ecol. Prog. Ser. 216, 265–278.

1 l Indic

C

C

D

D

E

F

F

F

F

G

G

G

H

H

H

J

K

L

L

L

L

L

L

M

M

MM

M

M

M

M

66 M. Farré et al. / Ecologica

olgate, J.E., Lynch, K.M., 2004. Mechanics and control of swimming: a review. IEEEJ. Ocean. Engineering 29 (3), 660–673.

ornwell, W.K., Schwilk, D.W., Ackerly, D., 2006. A trait-based test for habitat filter-ing: convex hull volume. Ecology 87 (6), 1465–1471.

avis, M.P., Chakrabarty, P., 2011. Tripodfih (Aulopiformes: Bathypterois) locomo-tion and landing behaviour from video observation at bathypelagic depths inthe Campos Basin of Brazil. Mar. Biol. Res. 7 (3), 297–303.

umay, O., Tari, P.S., Tomasini, J.A., Mouillot, D., 2004. Functional groups oflagoon fish species in Languedoc Roussillon, southern France. J. Fish Biol. 64,970–983.

rnst, R., Linsenmair, K.E., Rodel, M.O., 2006. Diversity erosion beyond the specieslevel: dramatic loss of functional diversity after selective logging in two tropicalamphibian communities. Biol. Conserv. 133, 143–155.

lynn, D.F.B., Gogol-Prokurat, M., Nogeire, T., Molinari, N., Richers, B.T., Lin, B.B.,Simpson, N., Mayfield, M.M., DeClerck, F., 2009. Loss of functional diversity underland use intensification across multiple taxa. Ecol. Lett. 12, 22–33.

oote, M., 1993. Discordance and concordance between morphological and taxo-nomic diversity. Paleobiology 19, 185–204.

roese, R., Pauly, D. (Eds.), 2011. FishBase. World Wide Web electronic publication,www.fishbase.org

ulton, C.J., Bellwood, D.R., Wainwright, P.C., 2001. The relationship between swim-ming ability and habitat use in wrasses (Labridae). Mar. Biol. 139, 25–33.

erber, S., Eble, G.J., Neige, P., 2008. Allometric space and allometric disparity: adevelopmental perspective in the macroevolutionary analysis of morphologicaldisparity. Evolution 62, 1450–1457.

ray, J.S., 2000. The measurement of marine species diversity, with an applicationto the benthic fauna of the Norwegian continental shelf. J. Exp. Mar. Biol. Ecol.250, 23–49.

osline, W.A., 1994. Function and structure in the paired fins of scorpaeniform fishes.Environ. Biol. Fish. 40, 219–226.

alpern, B.S., Floeter, S.R., 2008. Functional diversity responses to changing speciesrichness in reef fish communities. Mar. Ecol. Prog. Ser. 364, 147–156.

eino, J., Soininen, J., Lappalainen, J., Virtanen, R., 2005. The relationship betweenspecies richness and taxonomic distinctness in firewater organisms. Limnol.Oceanogr. 50 (3), 978–986.

elfman, G.S., Collette, B.B., Facey, D.E., 1997. The Diversity of Fishes. BlackwellScience, Oxford.

enkins, R.E., Burkhead, N.M., 1993. Freshwater Fishes of Virginia. American FisheriesSociety, Bethesda, Maryland.

asumyan, A.O., 2011. Tactile reception and behavior of fish. J. Ichthyol. 51 (11),1035–1103.

angerhans, R.B., Layman, C.A., Shokrollahi, A.M., DeWitt, T.J., 2004. Predator-drivenphenotypic diversification in Gambusia affinis. Evolution 58, 2305–2318.

ombarte, A., Aguirre, H., 1997. Quantitative differences in the chemorecep-tors systems in the barbels of two species of Mullidae (Mullus surmuletusand M. barbatus) with different bottom habitats. Mar. Ecol. Prog. Ser. 150,57–64.

ombarte, A., Olaso, I., Bozzano, A., 2003. Ecomorphological trends in Artedidra-conidae (Pisces: Perciformes: Notothenioidei) of the Weddell Sea. Antartic Sci.15, 211–218.

ombarte, A., Palmer, M., Matallanas, J., Gómez-Zurita, J., Morales-Nin, B., 2010.Ecomorphological trends and phylogenetic inertia of otolith sagittae inNototheniidae. Environ. Biol. Fish. 89, 607–618.

ombarte, A., Gordoa, A., Whitfield, A.K., James, N.C., Tuset, V.M., 2012. Ecomorpho-logical analysis as a complementary tool to detect changes in fish communitiesfollowing major perturbations in two South African estuarine systems. Environ.Biol. Fish. 94 (4), 601–614.

yashevska, O., Farnsworth, K.D., 2012. How many dimensions of diversity do weneed? Ecol. Indic. 18, 485–492.

cClain, C.R., Johnson, N.A., Rex, M.A., 2004. Morphological disparity as a biodiver-sity metric in lower bathyal and abyssal gastropod assemblages. Evolution 58(2), 338–348.

agurran, A.E., 1988. Ecological Diversity and Its Measurement. Princeton Univer-sity Press, New Jersey.

argalef, R., 1958. Information theory in ecology. Gen. Syst. 3, 36–71.ason, N.W.H., Lanoiselee, C., Mouillot, D., Irz, P., Argillier, C., 2007. Functional char-

acters combined with null models reveal inconsistency in mechanisms of speciesturnover in lacustrine fish communities. Oecologia 153, 441–452.

artín, P., Maynou, F., Stelzenmüller, V., Sacanell, M., 2012. A small scale fisherynear a rocky littoral marine reserve in the northwestern Mediterranean (MedesIslands): yield (catches?) after two decades of fishing prohibition. Sci. Mar. 76(3), 607–618.

aynou, F., Recasens, L., Lombarte, A., 2011. Fishing tactics dynamics of a Mediter-

ranean small-scale coastal fishery. Aquat. Living Resour. 24, 149–159.

ercader, L., Lloris, D., Rucabado, J.A., 2001. Tots els peixos del mar Catalá: Diagnosisi claus d’identificació. Institut d’Estudis Catalans, Barcelona.

icheli, F., Halpern, B.S., 2005. Low functional redundancy in coastal marine assem-blages. Ecol. Lett. 8, 391–400.

ators 29 (2013) 159–166

Motta, P.J., Clifton, K., Hernandez, P., Eggold, B.T., 1995. Ecomorphological correlatesin ten species of subtropical seagrass fishes: diet and microhabitat utilization.Environ. Biol. Fish. 44, 37–60.

Mouillot, D., Gaillard, S., Aliaume, C., Verlanque, M., Belsher, T., Troussellier, M., DoChi, T., 2005. Ability of taxonomy diversity indices to discriminate coastal lagoonenvironments based on macrophyte communities. Ecol. Indic. 5, 1–17.

Muss, B.J., Nielsen, J.G., 1999. Sea Fish. Scandinavian Fishing Year Book, Hedehusene.Neige, P., 2003. Spatial patterns of disparity and diversity of the recent cuttlefishes

(Cephalopoda) across the Old World. J. Biogeogr. 30, 1125–1137.Nelson, J.S., 2006. Fishes of the World, fourth ed. Wiley and Sons, New Jersey.Olden, J.D., Poff, N.L., Bestgen, K.R., 2008. Trait synergisms and the rarity, extirpation,

and extinction risk of desert fishes. Ecology 89, 847–856.Pavoine, S., Vallet, J., Dufour, A.-B., Gachet, S., Daniel, H., 2009. On the challenge of

treating various types of variables: application for improving the measurementof functional diversity. Oikos 118, 391–402.

Pease, A.A., Ginzález-Díaz, A.A., Rodiles-Hernández, R., Winemiller, K.O., 2012. Func-tional diversity and trait-environment relationships of stream fish assemblagesin a large tropical catchment. Fresh. Res. 57 (5), 1060–1075.

Petchey, O.L., Gaston, K.J., 2006. Functional diversity: back to basics and lookingforward. Ecol. Lett. 9, 741–758.

Pielou, E.C., 1975. Ecological Diversity. Wiley, New York.Price, S.A., Holzman, R., Near, T.J., Wainwright, P.C., 2011. Coral reefs promote the

evolution of morphological diversity and ecological novelty in labrid fishes. Ecol.Lett. 14, 462–469.

Recasens, L., Lombarte, A., Sánchez, P., 2006. Teleostean fish composition and struc-ture of an artificial reef and a natural rocky area in Catalonia (North WesternMediterranean). Bull. Mar. Sci. 78, 71–82.

Ricklefs, R.E., 2010. Evolutionary diversification, coevolution between populationsand their antagonists, and the filling of niche space. Proc. Natl. Acad. Sci. U.S.A.107 (4), 1265–1272.

Rohlf, F.J., 2001. TPS Dig 1.31 and TPS Relative Wards Software. State University ofNew York at Stony Brook.

Rohlf, F.J., Marcus, L.F., 2003. A revolution in morphometrics. Trends Ecol. Evol. 8,129–132.

Roy, K., Foote, M., 1997. Morphological approaches to measuring biodiversity. TrendsEcol. Evol. 12, 277–281.

Roy, K., Balch, D.P., Helberg, M.E., 2001. Spatial patterns of morphological diversityacross the Indo-Pacific: analyses using strombid gastropods. Proc. R. Soc. Lond.B. 268, 2503–2508.

Schoener, T.W., 1974. Resource partitioning in ecological communities. Science 185,27–39.

Shannon, C.E., Weaver, W., 1949. The Mathematical Theory of Communication. Uni-versity of Illinois Press, Illinois.

Simpson, E.H., 1949. Measurement of species diversity. Nature 163, 688.Somerfield, P.J., Clarke, R., Warwick, R.M., Dulvy, N.K., 2008. Average functional dis-

tinctness as a measure of the composition of assemblages. ICES J. Mar. Sci. 65,1462–1468.

Stelzenmüller, V., Maynou, F., Martín, P., 2009. Patterns of species and functionaldiversity around a coastal marine reserve: a fisheries perspective. Aquat. Con-serv. Mar. Fresh. Ecosyst. 19, 554–565.

Villéger, S., Ramos Miranda, J., Flores Hernandez, D., Mouillot, D., 2010. Contrastedchanges in taxonomic and functional diversity of tropical fish communities afterhabitat degradation. Ecol. Appl. 20, 1512–1522.

Villéger, S., Novack-Gottshall, P.M., Mouillot, D., 2011. The multidimensionality ofthe niche reveals functional diversity changes in benthic marine biotas acrossgeological time. Ecol. Lett. 14, 561–568.

Visalakshi, N.K., Thangavel, K., 2009. Impact of normalization in distributed K-meansclustering. Inter. J. Soft Comput. 4, 168–172.

Wagner, C.E., McIntyre, P.B., Buels, K.S., Gilbert, D.M., Michel, E., 2009. Diet predictsintestine length in Lake Tanganyika’s cichlid fishes. Funct. Ecol. 23, 1122–1131.

Wainwright, P.C., Richard, B.A., 1995. Predicting patterns of prey use from morphol-ogy of fishes. Environ. Biol. Fish. 44, 97–113.

Wainwright, P.C., Bellwood, D.R., Westneat, M.W., 2002. Ecomorphology of locomo-tion in labrid fishes. Environ. Biol. Fish. 65, 47–62.

Warwick, R.M., Clarke, K.R., 1995. New ‘biodiversity’ measures reveal a decrease intaxonomic distinctness with increasing stress. Mar. Ecol. Prog. Ser. 129, 301–305.

Webb, P.W., 1984. Body form, locomotion and foraging in aquatic vertebrates. Am.Zool. 24, 107–120.

Winemiller, K.O., 1991. Ecomorphological diversification in lowland fresh-water fishassemblages from 5 biotic regions. Ecol. Monogr. 61, 343–365.

Willis, S.C., Winemiller, K.O., Lopez-Fernandez, H., 2005. Habitat structural complex-ity and morphological diversity of fish assemblages in a Neotropical floodplainriver. Oecologia 142, 284–295.

Wilsey, B.J., Chalcraft, D.R., Bowles, C.M., Willig, M.R., 2005. Relationships amongindices suggest that richness is an incomplete surrogate for grassland biodiver-sity. Ecology 86 (5), 1178–1184.

Zelditch, M.L., Sheets, H.D., Fink, W.L., 2003. The ontogenic dynamics of shape dis-parity. Paleobiology 29, 139–156.