sm80n2-4280

INTRODUCTIONTop The use of morphology emerged as a scientific discipline in the early 20th century based on the ‘form, shape, and function’ of species morphologies (, ). From then until now, this approach has been applied in many studies and has been suggested as a useful tool for better understanding the mechanisms promoting species coexistence, for inferring the ecological strategies of species (e.g. , ) and for investigating fluctuations within a community due to natural or anthropogenic perturbations (, ). Initially, changes in shape of species were assessed from linear measurements (, , ). In the 1990s, a new approach, called geometric morphometrics, allowed analyzing shape variation and its covariation (, , ). These methods quantify the shape variation of objects from the Cartesian coordinates of anatomical landmarks, unifying a rigorous statistical theory with analytical procedures for superimposing landmark configurations of all specimens in a common coordinate system. Therefore, in addition to dealing with issues related to allometry and isometry, the power of this method lies in the ability to detect and visualize shape differences more clearly than classical approaches (, ). In landmark-based studies, a common “conflict” arises in the definition of the number and position of the homologous landmarks between organisms (, , ). This is an important setback because the use of different criteria to define landmarks can influence results (, , ). In fishes, most landmark sets have mainly focused on feeding and locomotion apparatus, which are considered the primary drivers of coexistence and specific richness (e.g. , , ), and on body shape, which is a multitasking factor contributing to multiple traits, such as swimming, food capture, evading predators, courtship, defending territories and spawning (e.g. , , ). Other anatomical parts, such as pelvic and pectoral fins or sensorial organs (barbels or illicia), are not usually included because they are not rigid structures and are difficult to preserve intact, and it is often difficult to define homologous positions of these structures between species (, ). However, it has been demonstrated that these structures are key factors in many fish behaviours (), such as movement and body position (), prey capture () and receiving chemical stimuli (), so they are important in the functional and ecological role of species within communities. Although many researchers currently question their use in landmark methods, claiming that they are not solid structures, that they have highly variable positions that are difficult to standardize, or even that some of them are absent (), fins have been applied in evolutionary (, ), phylogenetic (), ecological (, , ) and biodiversity studies (). Morphological variations between organisms can be plotted in a morphospace where the species distribution and occupied space provide useful information on the structural complexity of the community (, ). There are three main approaches for exploring the distribution of points within space: indices of morphological disparity, quadrant analyses (based on density of points), and nearest-neighbour analyses (based on distance between points and their association), which can be represented in numerical measurements or graphical analysis (, , ). Numerical methods such as the morphological disparity (e.g. sum of range, sum of variance or position of centroid), the average link length of a minimum spanning tree, the average distance between any species and its nearest neighbour and participation ratio, do not provide the relative position of points in morphospace (, , ). Nevertheless, it is possible to infer whether data follow random, uniform, or clustered spatial patterns (). By contrast, graphical approaches are alternative ways for improving the knowledge of the distribution of points. The convex hull is useful to detect species contributing to a greater extent to the diversity of morphospace (, ). However, this approach is unable to assess the internal structure of morphospace (, ). The analysis of the distribution of points within morphospace is usually performed using the Euclidean minimum spanning tree, the relative neighbourhood graph, the nearest-neighbour graph, the Gabriel graph or kernel density, which display the main groupings of species and gaps where species are absent (, ; ). Similarly, Ripley’s function infers on the spatial distribution pattern (random, uniform, or clustered) (, , ). Our hypothesis was that the selection scheme of landmarks and semilandmarks can affect the morphospace configuration of fish assemblages. In addition, we wanted to determine the analytical methods that best describe the distribution and occupation of morphospaces. Therefore, the aims of the present study were (i) to apply, assess and compare different landmark and semilandmark schemes used in previous fish studies to discern the differences between the criteria and determine how the selection of landmarks influences the distribution patterns of fishes within the morphospace, and (ii) to analyse the internal structure of morphospaces using different analytical methods and then identify those that provide most complete information in order to explain the organization of fish species within the mosphospaces. MATERIALS AND METHODSTop Fish collection A fish database of a coastal assemblage of the Catalan Coast, northwestern Mediterranean (40.5-42.5°N, 0.30-3.25°E) was used for this study. In particular, an artificial reef-sandy assemblage of the central coast (41°10.52’N, 1°35.16’E) was selected due to its high species richness, morphological richness and functional and taxonomical diversity (). This artificial reef consists of a group of modules that were installed between 1987 and 1998 to avoid illegal trawling (). Fishes were collected bi-monthly by small-scale vessels with trammel nets in 2000-2003. The characteristics of trammel net fisheries in the study area are described in the literature (, ). For each fishing operation, the entire fish catch (commercial plus discarded fraction) was retained. A total of 48 species were identified (, ) (Table 1) and photographed (only one specimen per species). The photographic record of species was carried out using a standardized and homologous body position for all species (facing left), including fins and sensorial organs position and size, in order to avoid differences in the disposition of the anatomical structures between species that allow the application and comparison of different landmark-based methods in the morphological analyses (see Fig. 1).

Table 1. – List of species of the artificial reef-sandy assemblage analysed in this study (Calafell, Catalan coast, NW Mediterranean), taxonomical classification of the species (order and family) and the acronym used for each species.

Order	Family	Specie	Acronym
Anguilliformes	Congridae	Conger conger	Ccon
Gadiformes	Merlucciidae	Merluccius merluccius	Mmer
Gadiformes	Phycidae	Phycis phycis	Pphy
Lophiiformes	Lophiidae	Lophius piscatorius	Lpis
Mugiliformes	Mugilidae	Chelon labrosus	Clab
Mugiliformes	Mugilidae	Liza aurata	Laur
Mugiliformes	Mugilidae	Mugil cephalus	Mcep
Mugiliformes	Mugilidae	Liza ramada	Lram
Perciformes	Moronidae	Dicentrarchus labrax	Dlab
Perciformes	Serranidae	Serranus cabrilla	Scab
Perciformes	Carangidae	Seriola dumerili	Sdum
Perciformes	Haemulidae	Pomadasys incisus	Pinc
Perciformes	Sparidae	Boops boops	Bboo
Perciformes	Sparidae	Dentex dentex	Dden
Perciformes	Sparidae	Diplodus annularis	Dann
Perciformes	Sparidae	Diplodus cervinus	Dcer
Perciformes	Sparidae	Diplodus sargus	Dsar
Perciformes	Sparidae	Diplodus vulgaris	Dvul
Perciformes	Sparidae	Lithognathus mormyrus	Lmor
Perciformes	Sparidae	Pagellus acarne	Paca
Perciformes	Sparidae	Pagellus bogaraveo	Pbog
Perciformes	Sparidae	Pagellus erythrinus	Pery
Perciformes	Sparidae	Pagrus pagrus	Ppag
Perciformes	Sparidae	Sparus aurata	Saur
Perciformes	Sparidae	Spondyliosoma cantharus	Scan
Perciformes	Sciaenidae	Sciaena umbra	Sumb
Perciformes	Sciaenidae	Umbrina canariensis	Ucan
Perciformes	Sciaenidae	Umbrina cirrosa	Ucir
Perciformes	Mullidae	Mullus barbatus	Mbar
Perciformes	Mullidae	Mullus surmuletus	Msur
Perciformes	Pomacentridae	Chromis chromis	Cchr
Perciformes	Labridae	Coris julis	Cjul
Perciformes	Labridae	Labrus merula	Lmer
Perciformes	Uranoscopidae	Uranoscopus scaber	Usca
Pleuronectiformes	Scophthalmidae	Scophthalmus rhombus	Srho
Pleuronectiformes	Scophthalmidae	Psetta maxima	Pmax
Pleuronectiformes	Bothidae	Arnoglossus laterna	Alat
Pleuronectiformes	Bothidae	Bothus podas	Bpod
Pleuronectiformes	Citharidae	Citharus linguatula	Clin
Pleuronectiformes	Soleidae	Microchirus variegatus	Mvar
Pleuronectiformes	Soleidae	Solea lascaris	Slas
Pleuronectiformes	Soleidae	Solea senegalensis	Ssen
Pleuronectiformes	Soleidae	Solea solea	Ssol
Pleuronectiformes	Soleidae	Synaptura lusitanica	Slus
Scorpaeniformes	Scorpaenidae	Scorpaena notata	Snot
Scorpaeniformes	Scorpaenidae	Scorpaena porcus	Spor
Scorpaeniformes	Scorpaenidae	Scorpaena scrofa	Sscr
Scorpaeniformes	Triglidae	Trigla lucerna	Tluc

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Fig. 1. – Location of the selected landmarks and semilandmarks on the left side of standardized images for the three different methods used (morphospace 1, 2 and 3). The description of the meaning of landmarks for each method is detailed in Table 2. In method 1 (morphospace 1), landmark 15 is a semilandmark; in method 2 (morphospace 2), landmarks 16, 17, 18 and 24 are semilandmarks; and in method 3 (morphospace 3), landmarks 3, 10, 11, 13, 15, 18, 19, 20, 23, 24, 25 and 27 are semilandmarks. The species in the images is Mullus surmuletus.

Building morphospaces Morphological diversity of fish shapes was quantified using geometric morphometric techniques (, , ). The selection of landmarks and semilandmarks was based on commonly used choices in previous morphometric studies in fishes. Three different criteria considering all the recorded options in the bibliography were used (a detailed description of the meaning of landmarks and semilandmarks for each method is provided in Table 2): first, defining basically the body and head shape, widely used and considered to best represent the external shape of the body morphology (Fig. 1A, morphospace 1) (, , ); second, adding the eye size and the position of fins and caudal fin shapes, traits often recorded in paleontological and evolutionary studies given their ecological and functional importance (Fig. 1B, morphospace 2) (, ); and third, a partial combination of the first two methods, including the shape, size and position of all fins and sensorial organs (Fig. 1C, morphospace 3) (, , ).

Table 2. – Description of the morphological meaning of the selected landmarks and semilandmarks for the three methods analysed. Landmark scheme of morphospace 1 based on , and ; morphospace 2 based on and ; and morphospace 3 based on , and . In bold the exclusive landmarks of each morphospace; the remaining ones are homologous in all the morphospaces.

Landmarks	Morphospace 1	Morphospace 2	Morphospace 3
1	Anterior tip of the mouth	Anterior tip of the snout bone	Anterior tip of the mouth
2	Posterior tip of the mouth	Anterior tip of the mouth	Posterior tip of the mouth
3	Central point in the midline through the eye	Posterior tip of the mouth	Distal tip of the barbel, placed at 80º of the body margin (or projection in the lower jaw inferior margin of the position of the hyomandibular insertion, when no barbels)
4	Ventral margin in the end of the head	Anterior margin in the maximum eye width	Anterior margin in the maximum eye width
5	Dorsal margin in the end of the head	Posterior margin in the maximum eye width	Posterior margin in the maximum eye width
6	Dorsal insertion of the pectoral fin	Dorsal margin of the head exactly above the centre of the eye	Ventral margin in the end of the head
7	Insertion of the pelvic fin	Ventral margin in the end of the head	Posterior margin in the end of the head
8	Anterior insertion of the anal fin	Posterior margin in the end of the head	Dorsal margin in the end of the head
9	Posterior insertion of the anal fin	Dorsal margin in the end of the head	Central point in the baseline of the pectoral fin
10	Ventral insertion of the caudal fin	Dorsal insertion of the pectoral fin	Posterior tip of the pectoral fin when the fin is in position of maximum extension
11	Posterior margin of the caudal peduncle	Ventral insertion of the pectoral fin	Ventral margin of the pectoral fin when the fin is in position of maximum extension
12	Dorsal insertion of the caudal fin	Insertion of the pelvic fin	Insertion of the pelvic fin
13	Posterior insertion of the dorsal fin (second dorsal fin if exists)	Anterior insertion of the anal fin	Distal tip of the pelvic fin when the fin is in position of maximum extension
14	Anterior insertion of the dorsal fin (first dorsal fin if second fin exists)	Posterior insertion of the anal fin	Anterior insertion of the anal fin
15	Point of maximum body height in the body margin	Ventral insertion of the caudal fin	Distal tip from the anterior insertion of the anal fin when the fin is in position of maximum extension
16	-	Distal tip of the ventral lobe of the caudal fin when the fin is in position of maximum extension	Posterior insertion of the anal fin
17	-	Posterior margin of the caudal fin between dorsal and ventral lobes	Ventral insertion of the caudal fin
18	-	Distal tip of the dorsal lobe of the caudal fin when the fin is in position of maximum extension	Distal tip of the ventral lobe of the caudal fin when the fin is in position of maximum extension
19	-	Dorsal insertion of the caudal fin	Posterior margin of the caudal fin between dorsal and ventral lobes
20	-	Posterior insertion of the dorsal fin (second dorsal fin if it exists)	Distal tip of the dorsal lobe of the caudal fin when the fin is in position of maximum extension
21	-	Anterior insertion of the second dorsal fin (if it exists)	Dorsal insertion of the caudal fin
22	-	Posterior insertion of the first dorsal fin (if second dorsal fin exists)	Posterior insertion of the dorsal fin (second dorsal fin if it exists)
23	-	Anterior insertion of the dorsal fin (first dorsal fin if second fin exists)	Posterior tip of the dorsal fin (second dorsal fin if it exists) when the fin is in position of maximum extension
24	-	Point of maximum body height in the body margin	Transition point between spines and soft rays in the dorsal fin (if only one dorsal fin exists) or central point in the gap between the two dorsal fins on the dorsal margin of the body (if second dorsal fin exists)
25	-	-	Distal tip of the first spine of the dorsal fin (first dorsal fin if second fin exists) when the fin is in position of maximum extension
26	-	-	Anterior insertion of the dorsal fin (first dorsal fin if second fin exists)
27	-	-	Point of maximum body height in the body margin

We digitized the landmarks and semilandmarks in one standardized image of the left profile of species previously obtained as a consensus figure from different specimens using tpsDig v. 2.16 software () for geometric morphometric analysis. However, direct analysis of landmark coordinates contains other components unrelated to shape, such as position, orientation and size (). To remove these distortions, a generalized least-squares superimposition (GLS) procedure (generalized Procrustes) was applied, translating all specimens to a common centroid position in the coordinate system, scaling them to unit centroid size and rotating them to minimize the distances between corresponding landmarks (, ). Thus, working on standardized consensus images, superimposition methods allow the analysis of morphology independently of size (). However, GLS Procrustes coordinates are not expressed in Euclidean shape space. Therefore, these coordinates should be previously projected in a Euclidean tangent space in order to test whether the shape variation is small enough to consider that this new tangent space is a good representation of the Procrustes data in a Euclidean space (). Then, to check whether the variation between the two spaces was minimum for our communities, we computed the correlations between the tangent and Procrustes distances using tpsSmall v. 1.28 (). The results of correlations (Morphospace 1: uncentred correlation=0.999988, root mean square error=0.000419; Morphospace 2: uncentred correlation=0.999993, root mean square error=0.000404; Morphospace 3: uncentred correlation=0.999979, root mean square error=0.000639) confirmed that for the three methodologies both spaces were nearly identical. We then conducted a relative warp analysis of superimposed images using tpsRelw v. 1.49 (). Each relative warp axis represents a set of specific morphological characteristics, allowing particular morphological attributes of species to be analysed directly (e.g. , , ). Morphospaces were built from relative warps 1, 2 and 3 in order to capture the maximum and most important information about shape variation (). These first three relative warps attained 84.7% (morphospace 1), 81.7% (morphospace 2) and 78.6% (morphospace 3) of total morphological variability, considered a sufficiently representative percentage of the total amount of explained morphological variation. Finally, we reduced the three axes to two by applying a non-metric multidimensional scaling technique (nMDS) () to obtain a multidimensional graphical representation of the distribution of fish assemblages based on their similarity. Point pattern analyses Three types of descriptor were used to establish the spatial variability in the morphospaces: indices of morphological disparity, quadrant analyses and nearest-neighbour analyses (a complete scheme of the different analyses performed is shown in Figure 2). The last two analyses included numerical and graphical methods.

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Fig. 2. – Explanatory scheme for the procedure of analysis of morphospaces, including all the analyses and methods used.

Among the various disparity measures described in the literature (, , ), the following two indices of morphological disparity were used: a) Sum of variance (SOV), which is the sum of the variances along all the morphospace axes; and b) Sum of range of the middle two quartiles (QSOR), which defines the area that is occupied by the middle two quartiles of the data points along the x and y axes, providing information about the distribution pattern of points within the morphospace. In the quadrant analyses, it was estimated the variance-to-mean ratio (VRM), a numerical method which is defined as the ratio of variance to the mean:

VRM = \frac{σ^{2}}{μ}

when VMR>1, it corresponds to a clustered distribution; when VMR<1, it denotes an ordered distribution; and when VMR≈1, the distribution is uniform or more exactly follows a Poisson random distribution. A Kolmogorov-Smirnov (K-S test) test was applied to compare the observed and expected frequencies derived from the Poisson frequency distribution (Anderson et al. 1982Anderson R.M., Gordon D.M., Crawley M.J., et al. 1982. Variability in the abundance of animal and plant species. Nature 296: 245-248.). Graphically, the kernel density was determined for the localization of clusters of species using a Gaussian function:

f (x, y) = \frac{1}{2 π r^{2}} \sum_{i} e^{- \frac{d_{i}^{2}}{2 r^{2}}}

where

$d_{i} = \sqrt{{(x - x_{i})}^{2} + {(y - y_{i})}^{2}}$ ,

x and y are the coordinates of points, and r is the radius (Worton 1989Worton B.J. 1989. Kernel methods for estimating the utilization distribution in home-range studies. Ecology 70: 164-168., Fortin et al. 2005Fortin M.J., Keitt T.H., Maurer B.A., et al. 2005. Species’ geographic ranges and distributional limits: pattern analysis and statistical issues. Oikos 108: 7-17., Werdelin and Lewis 2013Werdelin L., Lewis M.E. 2013. Temporal change in functional richness and evenness in the Eastern African Plio-Pleistocene carnivoran guild. PLoS ONE 8: e57944.).

For nearest-neighbour analyses, the numerical value of the Clark-Evans nearest neighbour was obtained to compare the average distance between nearest neighbours with the expected distance in the case of random distribution using the following equation:

r = \frac{mean distance}{\frac{1}{2} \sqrt{density}}

when r<1, points are clustered; when r=1, the points occur in a random distribution; and when r>1, the points are evenly dispersed (Davis 1986Davis J.C. 1986. Statistics and data analysis in geology. John Wiley & Sons, New York., Werdelin and Lewis 2013Werdelin L., Lewis M.E. 2013. Temporal change in functional richness and evenness in the Eastern African Plio-Pleistocene carnivoran guild. PLoS ONE 8: e57944.). The expected (theoretical) distribution under the null hypothesis is plotted as a continuous curve together with the histogram of observed distances. The expected probability density function as a function of distance r is:

g (r) = 2 ρ π r e^{(- ρ π r^{2})}

where ρ=n/A is the density, n is number of points, and A is the estimation area from the convex hull (Clark and Evans 1954Clark P.J., Evans F.C. 1954. Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35: 445-453., Cornwell et al. 2006Cornwell W.K., Schwilk D.W., Ackerly D.D. 2006. A trait-based test for habitat filtering: convex hull volume. Ecology 87: 1465-1471.). In addition, it was also estimated the Ripley function, a graphical method that is also related to the nearest-neighbour distribution function and incorporates local variations, defined as:

\hat{K} (d) = \frac{1}{λ n} \sum_{i = 1}^{n} \sum_{j \neq 1} I (d_{i j} < d)

where d_ij is the Euclidean distance between the i^th and j^th points in a data set of n points, and λ is the average density of points (Ripley 1979Ripley B.D. 1979. Tests of ‘randomness’ for spatial point patterns. J. Roy. Stat. Soc. B 41: 368-374., Dixon 2002Dixon P.M. 2002. Ripley’s K function. In: El-Shaarawi A.H., Piergorsch W.W. (eds), Encyclopedia of Environmetrics, vol. 3, John Wiley & Sons, New York, USA, pp. 1796-1803., Perry et al. 2006Perry G.L.W., Miller B.P., Enright N.J. 2006. A comparison of methods for the statistical analysis of spatial point patterns in plant ecology. Plant Ecol. 187: 59-82.). Finally, the localization of species clusters was obtained from the Gabriel graph, an undirected graph or beta-skeleton expressing one notion of proximity or nearness among points allowing the localization of packings. The graph consists of two sets: nodes or points; and edges or lines, which represent connections between pairs of nodes showing which points are clumped (Gabriel and Sokal 1969Gabriel K.R., Sokal R.R. 1969. A new statistical approach to geographic variation analysis. Syst. Biol. 18: 259-278., Dale and Fortin 2010Dale M.R.T., Fortin M.J. 2010. From graphs to spatial graphs. Annu. Rev. Ecol. Evol. Syst. 41: 21-38.).

Finally, another graphical method, the convex hull, was used to define which species contribute most to the morphological diversity of morphospaces, allowing us to determine the peripheral shape of morphospaces.

RESULTSTop

Species distribution within the morphospace

All the morphospaces showed a similar spatial variability in the species distribution, with matched or very similar convex hull configurations (Fig. 3D). Morphospaces 1 and 3 presented no species close to the morphospace core. However, two species packs formed by Perciformes-Scorpaeniformes and Pleuronectiformes were located to the right and left side, respectively, while other different groups, including Anguilliformes, Gadiformes, Lophiiformes and Mugiliformes, occupied their own isolated areas in the morphospace (Fig. 3A and 3C). Instead, morphospace 2 showed a different spatial organization that was more extended in the first axis, separating the fishes in relation to number and position of dorsal fins (Fig. 3B). Negative values mainly corresponded to species with a continuous dorsal fin along the body located in the anterior position; the morphospace core was occupied by benthic species with one shorter dorsal fin originating at the end of the head, as in some Perciformes (Labridae, Serranidae and Sparidae) and Scorpaeniformes; and positive values were associated with species with two dorsal fins, including a high variety of fishes, such as Gadiformes, Mugiliformes and some Perciformes (Scianidae, Mullidae or Carangidae) (Fig. 3B).

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Fig. 3. – Representation of the morphospace for the first (A), second (B) and third method (C) showing the main differentiated groups of species. Ccon, Conger conger; Cjul, Coris julis; Dvul, Diplodus vulgaris; Lpis, Lophius piscatorius; Msur, Mullus surmuletus; Sdum, Seriola dumerili; Spor, Scorpaena porcus; Tluc, Trigla lucerna; Usca, Uranoscopus scaber. Colour legend of dots: Perciformes in dark blue, Scorpaeniformes in red, Pleuronectiformes in brown, Gadiformes in light blue, Mugiliformes in yellow, Lophiiformes in grey and Anguilliformes in purple. D, superimposed representation of the convex hull structure of the morphospaces for the three methods analysed. Morphospace 1 in green, morphospace 2 in red and morphospace 3 in blue, n=48.

The second axis mainly distinguished between elongated (positive values) or rounded (negative values) body shapes in all of the morphospaces (Fig. 3A and 3C), although in morphospace 2 this distinction was less evident because the species were more compacted along the axis (Fig. 3B). However, the axis also separated species based on other morphological characteristics, such as the relation between head and body size (species with larger heads in relation to body size presenting negative values and species with smaller heads showing positive values) and the number and position of pelvic and dorsal fins. Morphospaces 1 and 3 were characterized by the isolation of the anglerfish (Lophius piscatorius, Lophiiformes), having a transformed first ray of the first dorsal. Moreover, nektonic (Seriola dumerili, Carangidae) and epibenthic (Mullus surmuletus, Mullidae) species with two dorsal fins were located slightly separated along the second axis compared with species with one dorsal fin (Diplodus vulgaris, Sparidae, or Scorpaena porcus, Scorpaenidae) (Fig. 3A and 3C). However, benthic species with specially adapted pectoral fins, such as Trigla lucerna, were isolated and clearly identified in morphospace 3 (Fig. 3C). Moreover, in morphospace 2 only Conger conger (Anguilliformes) showed an extreme distribution because of its specially elongated shape, as was also shown in all of the morphospaces, whereas all remaining species were concentrated between –0.1 and 0.1 values, confirming a greater compaction along the axis (Fig. 3B).

Accounting for the observed morphological variation

The SOV showed no differences among morphospaces, reaching similar values of variance (0.021) in the three cases. By contrast, the QSOR revealed greater disparity, attaining the highest value (0.028) in morphospace 2, which demonstrated a more dispersed distribution of points compared with the remaining morphospaces. Although morphospaces 1 and 3 yielded similar values (0.011 and 0.015, respectively), the lowest filled area in morphospace 1 denoted a more compacted distribution of points located in the two middle quartiles than morphospace 3.

The VMR yielded values of 8.83, 6.64 and 7.8 for morphospaces 1, 2 and 3, respectively, corresponding with clustered distributions in all cases (K-S test=0.4031, P=0.2267; K-S test=0.4031, P=0.2267; K-S test=0.4308, P=0.2267, respectively). The kernel analysis evidenced an area of high density in the three morphospaces (red-orange zone; Fig. 4A), which was formed by 12, 12 and 9 species in morphospaces 1, 2 and 3, respectively. Morphospace 2 displayed several medium-density zones separated from each other, whereas in morphospaces 1 and 3 the species were distributed more closely distributed among them, especially near the centroid, forming a main core zone and favouring the connection between lesser densities (Fig. 4A).

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Fig. 4. – Representation of the kernel density (A), the graphic of the Ripley’s function (B) and the Gabriel graphs (C) for the three methods analysed. In the kernel graphics, colour legend indicates the degree of density of species (maximum density in red, minimum density in dark blue). In the graph of the Ripley’s function, the two red curves represent the 95% confidence envelopes of the function; and the obtained curve is in black. In the Gabriel graphs, grey shading circles represent the main species packings, n=48.

The Clark-Evans approach also denoted a clustered distribution of species in morphospace 1 (Z=–3.6158, P<0.05, r=0.7), morphospace 2 (Z=–3.0809, P<0.05, r=0.743) and morphospace 3 (Z=–3.6312, P<0.05, r=0.6996). The Ripley’s function values were higher than those corresponding to spatial randomness, thus confirming the presence of clusters in the three morphospaces (Fig. 4B). The Gabriel graphs displayed two similar clusters of species in the three morphospaces (Fig. 4C). First, a limited number of flatfishes formed A_i clusters. Second, a noticeable packing of similar species (sparids, serranids, pomacentrids, haemulids, mullids and scorpaenids) configured the B_i clusters, showing a lesser distance and triangulation of points in morphospace 2 than in the remaining morphospaces. Finally, in morphospace 2, a third species packing was observed (C₂ cluster), formed exclusively by species similar to Bi clusters but with two dorsal fins, whereas morphospace 3 was similar to morphospace 1 and showed no C clusters.

DISCUSSIONTop

Landmark-based methods considering only overall body shape have often been used in studies analysing intraspecific or interspecific differences between taxonomically close species (Loy et al. 1999Loy A., Boglione C., Cataudella S. 1999. Geometric morphometrics and morpho-anatomy: a combined tool in the study of seabream (Sparus aurata, Sparidae) shape. J. Appl. Ichthyol. 15: 104-110., Valentin et al. 2002Valentin A., Sévigny J.M., Chanut J.P. 2002. Geometric morphometrics reveals body shape differences between sympatric redfish Sebastes mentella, Sebastes fasciatus and their hybrids in the Gulf of St Lawrence. J. Fish. Biol. 60: 857-875., Costa and Cataudella 2007Costa C., Cataudella S. 2007. Relationship between shape and trophic ecology of selected species of Sparids of the Caprolace coastal lagoon (Central Tyrrhenian sea). Environ. Biol. Fish. 78: 115-123.), showing that such variability is related to their ecological features (Loy et al. 2001Loy A., Bertelletti M., Costa C., et al. 2001. Shape changes and growth trajectories in the early stages of three species of the genus Diplodus (Perciformes, Sparidae). J. Morphol. 250: 24-33., Rüber and Adams 2001Rüber L., Adams D.C. 2001. Evolutionary convergence of body shape and trophic morphology in cichlids from Lake Tanganyika. J. Evol. Biol. 14: 325-332.). In local assemblages, the ecological relationships between species play a strong role in determining the morphological adaptations to many life habits (Clabaut et al. 2007Clabaut C., Bunje P.M.E., Salzburger W., et al. 2007. Geometric morphometric analyses provide evidence for the adaptative character of the Tanganyikan cichlid fish radiations. Evolution 61: 560-578., Ricklefs 2012Ricklefs R.E. 2012. Species richness and morphological diversity of passerine birds. Proc. Natl. Acad. Sci. USA 109: 14482-14487.), suggesting that there is a strong link between ecology and morphology of fish species (Douglas and Matthews 1992Douglas M.E., Matthews W.J. 1992. Does morphology predict ecology? Hypothesis testing within a freshwater stream fish assemblage. Oikos 65: 213-224., Willis et al. 2005Willis S.C., Winemiller K.O., Lopez-Fernandez H. 2005. Habitat structural complexity and morphological diversity of fish assemblages in a Neotropical floodplain river. Oecologia 142: 284-295., Cooper and Westneat 2009Cooper W.J., Westneat M.W. 2009. Form and function of damselfish skulls: rapid and repeated evolution into a limited number of trophic niches. BMC Evol. Biol. 9: 24.). Feeding preference is the main ecological factor influencing morphospace structuring, differentiating strict herbivores with small heads, omnivores with taller bodies and shorter caudal peduncles and predators with longer bodies adapted to swimming and larger heads and mouths (Cavalcanti et al. 1999Cavalcanti M.J., Monteiro L.R., Lopes P.R.D. 1999. Landmark-based morphometric analysis in selected species of serranid fishes (Perciformes: Teleostei). Zool. Stud. 38: 287-294., Kassam et al. 2003Kassam D.D., Adams D.C., Ambali A.J.D., et al. 2003. Body shape variation in relation to resource partitioning within cichlid trophic guilds coexisting along the rocky shore of Lake Malawi. Anim. Biol. 53: 59-70., Costa and Cataudella 2007Costa C., Cataudella S. 2007. Relationship between shape and trophic ecology of selected species of Sparids of the Caprolace coastal lagoon (Central Tyrrhenian sea). Environ. Biol. Fish. 78: 115-123.). However, body shape is also affected by habitat: rounded-shaped species are adapted to low activity, living in generalist habits, whereas elongated-shaped species swim in the water column (Clabaut et al. 2007Clabaut C., Bunje P.M.E., Salzburger W., et al. 2007. Geometric morphometric analyses provide evidence for the adaptative character of the Tanganyikan cichlid fish radiations. Evolution 61: 560-578., Farré et al. 2015Farré M., Lombarte A., Recasens L., et al. 2015. Habitat influence in the morphological diversity of coastal fish assemblages. J. Sea Res. 99: 107-117.). In fact, fishes with flattened or elongated shapes (eels or flatfishes) are usually located at the periphery of morphospaces (Friedman 2010Friedman M. 2010. Explosive morphological diversification of spiny-finned teleost fishes in the aftermath of the end-Cretaceous extinction. Proc. R. Soc. B 277: 1675-1683., Tuset et al. 2014Tuset V.M., Farré M., Lombarte A., et al. 2014. A comparative study of morphospace occupation of mesopelagic ﬁsh assemblages from the Canary Islands (North-eastern Atlantic). Ichthyol. Res. 61: 152-158., present study).

Although more studies should be performed on other fish communities to confirm these statements at a more general level, our results indicate that the use of specific landmarks and semilandmarks related to fins and external sensorial organs helps to clarify the differentiation between species because it provides more detailed and accurate morphological information on the species, which translates into differences in the species distribution within the morphospace structure. However, due to the lack of similar comparative studies of different landmark schemes, it is necessary to test this methodology in communities with different ecological characteristics to confirm the results of the present study. The most noticeable difference between morphospace 1 and 3 was related to fishes with special morphological structures. The isolated location of Trigla lucerna in morphospace 3 reinforces this assertion, as it presents an extremely extended pectoral fin with the first three rays transformed, involved in locomotion, substrate lodging or feeding strategies (Jamon et al. 2007Jamon M., Renous S., Gasc J.P., et al. 2007. Evidence of force exchanges during the six-legged walking of the bottom-dwelling fish, Chelidonichthys lucerna. J. Exp. Zool. 307A: 542-547.). Moreover, fishes with presence of sensorial chin barbels used for stimuli reception and finding of food items (Lombarte and Aguirre 1997Lombarte A., Aguirre H. 1997. Quantitative differences in the chemoreceptor systems in the barbels of two species of Mullidae (Mullus surmuletus and M. barbatus) with different bottom habitats. Mar. Ecol. Prog. Ser. 150: 57-64., Hutchings and Griffiths 2005Hutchings K., Griffiths M.H. 2005. Identity and distribution of southern African sciaenid fish species of the genus Umbrina. Afr. J. Mar. Sci. 27: 1-21.), such as Mullus spp. and Umbrina spp., were also slightly separated from the main group of Perciformes in relation to the morphospace. It is important to highlight the use of fin shape and sensorial organs (morphospace 3) for analysing the morphological structure of fish assemblages because these anatomical characteristics are common to many species. For example, needlefishes use their characteristic compact set of fins to alter the flows created by body movement, a defining feature of their locomotion strategy (Liao 2002Liao J.C. 2002. Swimming in needlefish (Belonidae): anguilliform locomotion with fins. J. Exp. Biol. 205: 2875-2884.); in burrowing eels, the caudal fin is intimately related to their digging ability (De Schepper et al. 2007De Schepper N., De Kegel B., Adriaens D. 2007. Morphological specializations in Heterocongrinae (Anguilliformes: Congridae) related to burrowing and feeding. J. Morphol. 268: 343-356.); whereas in pelagic eels the presence of a continuous fin resulting from the confluence of dorsal, caudal and anal fins improves swimming performance (Tytell and Lauder 2004Tytell E.D., Lauder G.V. 2004. The hydrodynamics of eel swimming I. Wake structure. J. Exp. Biol. 207: 1825-1841.); or flying fishes, which possess extremely long pectoral fins that enable gliding flights out of the water after self-propelled jumps to escape predators or to save locomotion costs (Davenport 1994Davenport J. 1994. How and why do flying fish fly? Rev. Fish Biol. Fish. 40: 184-214.). In addition, benthic species in contact with the substratum, such as frogfishes or many scorpionfishes, use synchronized movements of pectoral and pelvic fins to move over the substrate or to maintain static positions in defensive, alert or rest behaviours (Gosline 1994Gosline W.A. 1994. Function and structure in the paired fins of scorpaenifom fishes. Environ. Biol. Fish. 40: 219-226., Yamanoue et al. 2010Yamanoue Y., Setiamarga D.H.E., Matsuura K. 2010. Pelvic fins in teleosts: structure, function and evolution. J. Fish. Biol. 77: 1173-1208.), or transformations of pelvic fins into suction discs that help to the adherence on the substrate occurs in gobies (Schoenfuss and Blob 2003Schoenfuss H.L., Blob R.W. 2003. Kinematics of waterfall climbing in Hawaiian freshwater fishes (Gobiidae): vertical propulsion at the aquatic-terrestrial interface. J. Zool. 261: 191-205.). The reduction or modification of pelvic fins used during aggressive or courtship behaviours are also common in balistoids (Yamanoue et al. 2010Yamanoue Y., Setiamarga D.H.E., Matsuura K. 2010. Pelvic fins in teleosts: structure, function and evolution. J. Fish. Biol. 77: 1173-1208.), as well as the occurrence of spines in pelvic, dorsal and median fins for defence and propulsion purposes in gasterosteids and scorpionfishes (Gosline 1994Gosline W.A. 1994. Function and structure in the paired fins of scorpaenifom fishes. Environ. Biol. Fish. 40: 219-226.). By contrast, the identification of species by including the position of fins (morphospace 2) clearly influenced the morphospace distribution. In this case, although many species were located in a similar position in morphospaces 1 and 3, the variation in the number of dorsal fins conditioned the results. As this biological character is very important in taxonomy, phylogeny and evolution (Nelson 2006Nelson J.S. 2006. Fishes of the World, 4rth edition. Wiley and Sons, New Jersey.), it suggests that species distribution within morphospace 2 is not sufficient for a full ecological understanding.

The combination of different methods is the best approach for analysing the species occupation within morphospace (Perry et al. 2006Perry G.L.W., Miller B.P., Enright N.J. 2006. A comparison of methods for the statistical analysis of spatial point patterns in plant ecology. Plant Ecol. 187: 59-82., Van Bocxlaer and Schultheiß 2010Van Bocxlaer B., Schultheiß R. 2010. Comparison of morphometric techniques for shapes with few homologous landmarks based on machine-learning approaches to biological discrimination. Paleobiology 36: 497-515.). Depending on the aims of the study, all the methods have advantages and drawbacks, as discussed above, so an ensemble of different analyses is necessary to corroborate and complement outcomes and obtain a better understanding of the point patterns, thus avoiding the bias that may result from a specific chosen analysis (Wiegand and Moloney 2004Wiegand T., Moloney K.A. 2004. Rings, circles, and null-models for point pattern analysis in ecology. Oikos 104: 209-229., Perry et al. 2006Perry G.L.W., Miller B.P., Enright N.J. 2006. A comparison of methods for the statistical analysis of spatial point patterns in plant ecology. Plant Ecol. 187: 59-82.). Although numerical methods are needed to test the statistical significance of outcomes, they are restricted to interactions between points at short scales and can omit relationships at larger distances. Graphical methods are recommended at larger scales because they provide a better and more intuitive visual interpretation of morphospace shape (e.g. Perry et al. 2006Perry G.L.W., Miller B.P., Enright N.J. 2006. A comparison of methods for the statistical analysis of spatial point patterns in plant ecology. Plant Ecol. 187: 59-82., Werdelin and Lewis 2013Werdelin L., Lewis M.E. 2013. Temporal change in functional richness and evenness in the Eastern African Plio-Pleistocene carnivoran guild. PLoS ONE 8: e57944., Tuset et al. 2014Tuset V.M., Farré M., Lombarte A., et al. 2014. A comparative study of morphospace occupation of mesopelagic ﬁsh assemblages from the Canary Islands (North-eastern Atlantic). Ichthyol. Res. 61: 152-158.). In this study, the indices of morphological disparity did not provide enough information regarding the spatial distribution of species within morphospaces. The variance of the different axes changes among morphospaces, but their cumulative contribution can express similar total variability even if the morphospaces possess different configurations (Foote 1997Foote M. 1997. The evolution of morphological diversity. Annu. Rev. Ecol. Syst. 28: 129-152., Ciampaglio et al. 2001Ciampaglio C.N., Kemp M., McShea D.W. 2001. Detecting changes in morphospace occupation patterns in the fossil record: characterization and analysis of measures of disparity. Paleobiology 27: 695-715., Korn et al. 2013Korn D., Hopkins M.J., Walton S.A. 2013. Extinction space – A method for the quantification and classification of changes in morphospace across extinction boundaries. Evolution 67: 2795-2810.), as also shown in our results. Although calculating the areas occupied by points helps to analyse the distribution pattern of points, the measure does not permit the establishment of the specific location and occupation of these points within the morphospace. Therefore, these measurements fail in terms of estimating the distribution and occupation of morphospaces; it is better to use quadrant and nearest-neighbour analyses for these purposes (Shen et al. 2008Shen B., Dong L., Xiao S., et al. 2008. The Avalon explosion: evolution of Ediacara morphospace. Science 319: 81-84., Ricklefs 2012Ricklefs R.E. 2012. Species richness and morphological diversity of passerine birds. Proc. Natl. Acad. Sci. USA 109: 14482-14487., Tuset et al. 2014Tuset V.M., Farré M., Lombarte A., et al. 2014. A comparative study of morphospace occupation of mesopelagic ﬁsh assemblages from the Canary Islands (North-eastern Atlantic). Ichthyol. Res. 61: 152-158.).

Kernel density graphics represented a visual image of species distribution within morphospace and species density in a fixed area, which was a useful tool for delimiting the range of species and quantifying their occurrence probability in space (Silverman 1986Silverman B.W. 1986. Density estimation for statistics and data analysis. Monographs on Statistics and Applied Probability, Chapman and Hall, London., Worton 1989Worton B.J. 1989. Kernel methods for estimating the utilization distribution in home-range studies. Ecology 70: 164-168., Fortin et al. 2005Fortin M.J., Keitt T.H., Maurer B.A., et al. 2005. Species’ geographic ranges and distributional limits: pattern analysis and statistical issues. Oikos 108: 7-17.). The results showed slight differences between morphospaces, especially between 1 and 3, focusing mainly on the area shape of higher density (red colour in Fig. 4A). However, in morphospace 3, this area was arranged in a more elongated and straightened way than in morphospaces 1 and 2 due to species disaggregation favoured by the presence of landmarks defining the presence of chin barbels and a better differentiation of swimming species. However, this graphical representation is unable to establish the connections between close species and organize them in local clusters (Shen et al. 2008Shen B., Dong L., Xiao S., et al. 2008. The Avalon explosion: evolution of Ediacara morphospace. Science 319: 81-84., Werdelin and Lewis 2013Werdelin L., Lewis M.E. 2013. Temporal change in functional richness and evenness in the Eastern African Plio-Pleistocene carnivoran guild. PLoS ONE 8: e57944.). The variance-to-mean ratio and Ripley’s function determined a clustered distribution in the three morphospaces, but it cannot display the location of local clusters, thus limiting the perception of morphospace structure. In this context, the Gabriel graph allowed to identify the packing species based on the distances and connections between them. The Gabriel method likely provides a more complex graphical representation to be considered as an extension of the Euclidean minimum spanning tree, the relative neighbourhood graph, and the nearest-neighbour graph (Gabriel and Sokal 1969Gabriel K.R., Sokal R.R. 1969. A new statistical approach to geographic variation analysis. Syst. Biol. 18: 259-278.). Evidently, the selection of the clusters of species within the graph is partially subjective, but ecologically it is very interesting because graphs are able to describe important ecological characteristics of communities, such as structural complexity or relationships between species (Strogatz 2001Strogatz S.H. 2001. Exploring complex networks. Nature 410: 268-276., Dale and Fortin 2010Dale M.R.T., Fortin M.J. 2010. From graphs to spatial graphs. Annu. Rev. Ecol. Evol. Syst. 41: 21-38.), where species that are close together interact in the same environment and exploit similar resources. Thus, the packing of species can provide useful information about the internal dynamics within communities. Hence, we propose that natural directions for future research should include this topic in order to test the usefulness and abilities of this graphical method in the study of structural complexity of communities.

In conclusion, the results of this study suggest that depending on the purposes, several methods of landmark selection are able to display the distribution of species within morphospace (Adams et al. 2013Adams D.C., Rohlf F.J., Slice D.E. 2013. A field comes of age: geometric morphometrics in the 21st century. Hystrix It. J. Mamm. 24: 7-14.). However, including the maximum number of anatomical traits of species, especially those with special morphological adaptations, such as Stomiiformes, Lophiiformes, Ipnopidae (Aulopiformes), etc. (Tuset et al. 2014Tuset V.M., Farré M., Lombarte A., et al. 2014. A comparative study of morphospace occupation of mesopelagic ﬁsh assemblages from the Canary Islands (North-eastern Atlantic). Ichthyol. Res. 61: 152-158., Farré et al. 2015Farré M., Lombarte A., Recasens L., et al. 2015. Habitat influence in the morphological diversity of coastal fish assemblages. J. Sea Res. 99: 107-117.), can be important in order to better differentiate the species and characterize them ecologically, as our results demonstrate. Therefore, in analysis of fish communities where species of special morphologies may be present, the method considering fin shape and sensorial organs in the landmark configuration provides an appropriate and accurate description of the spatial occupancy of species, helping to improve the understanding of the structural complexity and ecological processes of fish assemblages. Moreover, although graphical methods are in general more intuitive and interpretable, a combination of different analytical methods, including numerical and graphical ones, is the best and most complete option for assessing the internal occupation of morphospaces.

ACKNOWLEDGEMENTSTop

Data of this study were financed by the projects “ESCAL 1” (Ref. PCC30004/99) and “ESCAL 2” (Ref. 02P30015) of the Directorate General of Fisheries of the Government of Catalonia, and “CONFLICT” (Ref. CGL2008-00047) of the Spanish National Research Council. Marc Farré Foix acknowledges a pre-doctoral grant and a third cycle tuition fee grant from the Government of Andorra, ATC-010-AND and AMTC010-AND/2013, academic years 2013-2014 and 2014-2015.

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