MEEGID 2043 No. of Pages 10, Model 5G 12 August 2014
Infection, Genetics and Evolution xxx (2014) xxx–xxx 1 Contents lists available at ScienceDirect
Infection, Genetics and Evolution
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5 6
3 Outline-based morphometrics, an overlooked method in arthropod
4 studies?
a,⇑ b c d d e 7 Q1 Jean-Pierre Dujardin , D. Kaba , P. Solano , M. Dupraz , K.D. McCoy , N. Jaramillo-O
8 a IRD, UMR IRD–CIRAD INTERTRYP, Campus international de Baillarguet, Montpellier, France 9 b Institut Pierre Richet, Bouake, Côte d’Ivoire 10 c IRD, UMR IRD–CIRAD INTERTRYP, CIRDES, Bobo-Dioulasso, Burkina Faso 11 d MIVEGEC, UMR 5290 IRD-CNRS, Centre IRD, Montpellier, France 12 e Instituto de Biologia, University of Antioquía, Medellin, Colombia
13 14 article info abstract 1630 17 Article history: Modern methods allow a geometric representation of forms, separating size and shape. In entomology, as 31 18 Received 9 April 2014 well as in many other fields involving arthropod studies, shape variation has proved useful for species 32 19 Received in revised form 25 July 2014 identification and population characterization. In medical entomology, it has been applied to very specific 33 20 Accepted 31 July 2014 questions such as population structure, reinfestation of insecticide-treated areas and cryptic species 34 21 Available online xxxx recognition. For shape comparisons, great importance is given to the quality of landmarks in terms of 35 comparability. Two conceptually and statistically separate approaches are: (i) landmark-based 36 22 Keywords: morphometrics, based on the relative position of a few anatomical ‘‘true’’ or ‘‘traditional’’ landmarks, 37 23 Modern morphometrics and (ii) outline-based morphometrics, which captures the contour of forms through a sequence of close 38 24 Landmarks 25 Outlines ‘‘pseudo-landmarks’’. 39 26 Arthropods Most of the studies on insects of medical, veterinary or economic importance make use of the landmark 40 27 Cryptic species approach. The present survey makes a case for the outline method, here based on elliptic Fourier analysis. 41 28 Population structure The collection of pseudo-landmarks may require the manual digitization of many points and, for this rea- 42 29 son, might appear less attractive. It, however, has the ability to compare homologous organs or structures 43 having no landmarks at all. This strength offers the possibility to study a wider range of anatomical struc- 44 tures and thus, a larger range of arthropods. 45 We present a few examples highlighting its interest for separating close or cryptic species, or charac- 46 terizing conspecific geographic populations, in a series of different vector organisms. In this simple appli- 47 cation, i.e. the recognition of close or cryptic forms, the outline approach provided similar scores as those 48 obtained by the landmark-based approach. 49 Ó 2014 Published by Elsevier B.V. 50 51
52 53 54 1. Introduction (or ‘‘anatomical’’, or ‘‘traditional’’) landmarks. True landmarks are 66 considered homologous. Homology here refers to the positional 67 55 Contrary to classical morphology, the main objective of mor- equivalence of a small biological structure, as small as a point at 68 56 phometrics is not to describe organisms, but to compare them. the requisite scale. The level of homology of a landmark is gov- 69 57 Because of the geometric constraints of modern morphometrics, erned by the precision with which it can be localized from one 70 58 data are generally collected on a non-articulated part, often a sin- organism to another. Thus, true, anatomical landmarks are homol- 71 59 gle organ (but see David et al. (1996) and Adams (1999)). A few ogous in the sense that they are relocatable points, and according 72 60 characters are enough, provided that they are homologous. to this criterion various levels of homology have been recognized 73 61 Insect species studies generally made use of the wings because (see type I, II and III landmarks, Bookstein (1991)). A special devel- 74 62 these structures are almost bidimensional and relatively rigid, opment of type III landmarks, called ‘‘semi-landmarks’’ (also ‘‘slid- 75 63 reducing digitizing error; also, importantly, because the wings of ing semi-landmarks’’) allows the description of curved lines 76 64 many groups of insects provide a large number of landmarks. between two classical landmarks (Bookstein, 1997). 77 65 Not only are they many, they also are of good quality, called ‘‘true’’ Anatomical, true landmarks are opposed to ‘‘pseudo-land- 78 marks’’ used in the outline-based approach. Pseudo-landmarks 79
⇑ Corresponding author. describing contours or boundary outlines do not depend on the 80 Q1 E-mail address: [email protected] (J.-P. Dujardin). presence of true anatomical landmarks, they can exist with no 81
http://dx.doi.org/10.1016/j.meegid.2014.07.035 1567-1348/Ó 2014 Published by Elsevier B.V.
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82 anatomical landmark at all, or can include one or more of them. the field, there is frequently some size overlap, so specimens col- 144 83 Obviously, pseudo-landmarks are of another nature than true land- lected from trees are assumed to be R. robustus, and those collected 145 84 marks: comparability is not expected from them separately, but from houses are assumed to be R. prolixus. Our material contains 146 85 from the structure they describe. Thus, if carefully chosen, like three groups previously identified by molecular tools (Monteiro 147 86 the mandible of a beetle, the genital paramere of a sandfly or an et al., 2003): one is R. prolixus from houses in the Pampanito village 148 87 internal cell of the wing, a contour represents an homologous (State of Trujillo, Venezuela), the second one is R. prolixus from 149 88 structure allowing interindividual and/or intergroup comparisons. palm trees collected in another locality, San José Tiznados (State 150 89 It is quite possible to develop both approaches on the same of Guárico, Venezuela), and the last one is R. robustus from two 151 90 organ (Baylac and Frieß, 2005). One can see the wings of an insect localities of Venezuela: Pampanito and Candelaria (State of 152 91 as a configuration of anatomical landmarks, but the wing can be Trujillo). 153 92 seen also as an assemblage of closed contours, i.e. the internal cells We performed analyzes both combining the two R. prolixus hab- 154 93 designated by the intersecting veins (Francoy et al., 2012). More- itats, as well as the two R. robustus origins, into single samples and 155 94 over, other anatomical parts, because they are generally deficient considering them as separated groups. A total of 7 landmarks could 156 95 in true landmarks, are only amenable to an outline analysis. For be used, thus excluding the eighth used in Feliciangeli et al. (2007) 157 96 instance, many insect species, including very close species, are sep- because of the inconsistency of the clavum position on the pic- 158 97 arated by the shape of some genital pieces. Such parts often lack tures. The outline submitted to the elliptical Fourier analysis 159 98 anatomical landmarks, like the paramere of sandflies, the prono- (EFA, see Section 2.2.2) contained 5 of them, as well as the external 160 99 tum of Coleoptera (Faille et al., 2007), the genital leaflet of Culex boundary of the membranous part of the wing (Fig. 1). 161 100 neavei (Boussès et al., 2013; Garros and Dujardin, 2013) or the 101 mandible parts of beetles (Tatsuta et al., 2009). Eggs and juvenile 2.1.2. Glossina palpalis palpalis and Glossina palpalis gambiensis 162 102 instars which also provide important taxonomic characters gener- G. p. palpalis (Robineau-Desvoidy), 1830 and G. p. gambiensis 163 103 ally lack acceptable configurations of true landmarks: they can be Vanderplank 1949 (Diptera: Glossinidae) are important vectors of 164 104 considered through outline analyzes. Also good or natural candi- sleeping sickness in West Africa. Both species hybridize readily in 165 105 dates for the outline approach are the many wingless forms of the laboratory but hybridized females produce fewer offspring 166 106 insects, as for instance fleas, lice, ants, many of them of great vet- and hybrid males are sterile (Gooding, 1988). These two subspecies 167 107 erinary, medical or agronomic importance. Finally, arthropods are difficult to separate on morphological ground. Although males 168 108 other than insects may present poorly defined landmarks but a show consistent differences in the terminal dilatation of inferior 169 109 particular anatomy which can be submitted to outline analyzes, claspers of their genitalia (Machado, 1954), morphological differ- 170 110 as for instance the rounded body of ticks. In sum, the outline entiation of female flies is not conclusive. The material we used 171 111 approach offers the possibility to study a wider range of organs, here is from Ivory Coast and was collected in 2007. Male and 172 112 along with a wider range of arthropods. female specimens of G. p. palpalis were collected in Aniassue, South 173 113 The question is not limited however to technical considerations of Ivory Coast. The G. p. gambiensis specimens came from Ganse, 174 114 about presence or absence of landmarks (Rohlf, 1986), it also con- North of Ivory Coast. The subspecies identity of all the female 175 115 cerns the biological relevance of outlines. For entomologists, the and male specimens of the present study had no morphological 176 116 primary needs are to distinguish species, especially cryptic species, nor molecular confirmation. 177 117 and to detect them where they are not expected. Conspecific, but Ten landmarks were used as in Kaba et al. (in press). The EFA 178 118 geographically or ecologically separated populations, are of inter- method was applied to the comparison of the central cell of the 179 119 est too, in order to design relevant control strategies (Kaba et al., wing which includes five landmarks (Fig. 2). This cell is considered 180 120 in press) or to detect the origin of reinfestation after treatment as having an important taxonomic significance for tsetse flies (De 181 121 (Feliciangeli et al., 2007; Hernández et al., 2013; Gaspe et al., la Rocque et al., 2002). 182 122 2013). Does the outline approach distinguish species and popula- 123 tions as well as the landmark approach? We present here a few 2.1.3. Anopheles strodei and Anopheles oswaldoi 183 124 examples showing no significant difference between the two A. oswaldoi (Peryassú) has an unclear taxonomic and vectorial 184 125 methods when it is about discriminating between close species status in South America (Ruiz-Lopez et al., 2013), and A. strodei 185 126 or between conspecific populations. Root shows a high levels of morphological polymorphism 186 (Bourke et al., 2013). Both species are difficult to distinguish based 187 on existing dichotomous keys (Faran, 1980; Faran and Linthicum, 188 127 2. Materials and methods 1981; Gonzales and Carrejo, 2007). Females were collected from 189 the municipality of Puerto Asis, department of Putumayo, Colom- 190 128 2.1. Material bia. They were induced to oviposit in the laboratory, where the 191 male genitalia and associated immature stages of offspring were 192 129 2.1.1. Rhodnius prolixus and Rhodnius robustus used for taxonomic identification. A. oswaldoi specimens were con- 193 130 R. prolixus Stal, 1859 and R. robustus Larrousse, 1927 are mor- firmed by PCR-RFLP of the rDNAmarker ITS-2 (Ruiz et al., 2005). 194 131 phologically very close species with a long history of controversy From A. oswaldoi, 30 adult F1 females and from A. strodei, 32 adult 195 132 among taxonomists (Bargues et al., 2010). R. prolixus is a major vec- F1 females were randomly chosen for the morphometric studies. 196 133 tor of Chagas disease in the northern part of Latin America, recently Eleven anatomical landmarks were selected on the crossing of 197 134 eradicated from Central America (Hashimoto and Schofield, 2012) wing veins, while the EFA method used the contour of a small cell 198 135 but still active in Venezuela and Colombia (Gorla et al., 2010). The at the internal base of the wing, a contour which did not include 199 136 two species generally are found in different ecological conditions: any of the eleven landmarks (Fig. 3). 200 137 in domestic and peridomestic structures for R. prolixus, in the 138 crown of palm trees for R. robustus. Because of its silvatic habit 2.1.4. Ornithodoros maritimus 201 139 preferences, R. robustus is not considered as an important vector O. maritimus is a soft tick (Family Argasidae) and part of the 202 140 of Chagas disease, but it is involved in some local transmission widespread species complex, Ornithodoros capensis sensu lato, 203 141 (Feliciangeli et al., 2002). The overall morphology of the two spe- exploiting colonial seabirds in tropical and sub-tropical areas of 204 142 cies is very similar, although striking size differences are often the world (Dietrich et al., 2011). The different members of this 205 143 observed, with R. robustus being generally the larger species. In complex are known vectors of several viruses and bacteria, 206
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Fig. 1. Top: Outline as digitized for a Rhodnius sp. wing, and seven landmarks (red dots). Bottom: Shape differences between outlines of R. prolixus and R. robustus. It can be seen that the selected outline remains stable within each species. The two samples of R. prolixus are almost identical in spite of having different sizes (see Fig. 5), different geographic origins (Pampanito, Guarico) and habitats (domestic, silvatic). A similar observation can be made for the two samples of R. robustus.
Fig. 2. Top: Outline as digitized for a Glossina sp. wing, and 10 landmarks (red dots). Bottom: Shape differences between outlines of female Glossina palpalis palpalis and G. p. gambiensis. The contours are very similar, with the elongated part of the cell thinner for G. p. palpalis.
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Fig. 3. Top: Outline as digitized for an Anopheles sp. wing, and 11 landmarks (red dots). Bottom: Shape differences between outlines of A. strodei and A. oswaldoi.
207 including the agents responsible for tick-borne relapsing fever and the observed contour is decomposed in terms of sine and cosine 247 208 African tick bite fever (Takano et al., 2009; Dietrich et al., 2011, in curves of successive frequencies called harmonics, and each har- 248 209 press). Although the O. capensis complex currently includes eight monic is described by four coefficients. With this method (Kuhl 249 210 species, the actual number of species and their geographical distri- and Giardina, 1982), the first harmonic ellipse parameters are used 250 211 bution have been recently called into question (Gómez-Díaz et al., to standardize the Fourier coefficients so that they are invariant to 251 212 2012). Given the lack of reliable morphological characters for iden- size, rotation and the starting position of the outline trace. By doing 252 213 tifying soft tick species in the adult life stage (Estrada-Pena et al., this, the three first coefficients become constant (1, 0 and 0) and 253 214 2010), alternative methods are greatly needed. In the present are not used in the remaining analyses. The fourth coefficient, 254 215 study, two distant French populations were analyzed, both within the one related to the width-on-length ratio of the outline 255 216 the described distribution of O. maritimus: one from Rouzic, one of (Lestrel, 1989), has been used in our study. 256 217 the islands of the Sept-Iles archipelago off the northern coast of As for the conventional Fourier analysis, the maximum number 257 218 Brittany and one from Riou, a Mediterranean island lying near of harmonics is half the number of sample points (pseudo-land- 258 219 the city of Marseilles. In Rouzic, 18 adult ticks were collected from marks). The optimal number of harmonics can be selected in vari- 259 220 around the nests of the Northern gannet (Morus bassanus), whereas ous ways: by appraising the goodness-of fit using the sum of 260 221 in Riou, the 20 adult ticks analyzed came from nests of the Yellow- squared distances between the original data and reconstructed 261 222 legged gull (Larus michahellis). outline, by examining the spectrum of harmonic Fourier power, 262 223 Fifteen landmarks were used on the ventral face of the ticks. The or simply by visualizing the reconstructed outlines (Lestrel, 1989, 263 224 EFA method used the contour of the external boundary of the dor- 1997; Claude, 2008). Ideally, more harmonics should be able to 264 225 sal face (Fig. 4). capture more shape parameters and produce a higher level of dis- 265 crimination. However, more harmonics also inflate the digitization 266 226 2.2. Statistical approaches error and an optimal number should be selected (Crampton, 1995; 267 Renaud et al., 1999; Firmat et al., 2010). In our study, the threshold 268 227 2.2.1. Landmark-based analyzes regarding the number of harmonics was governed by the problem 269 228 Shape variables were obtained through the generalized Procrus- of high-dimensionality in the resulting data set (see next section). 270 229 tes analysis (GPA) superimposition algorithm and subsequent pro- The superiority of the elliptic Fourier algorithm over other 271 230 jection of the Procrustes residuals into an euclidean space (Rohlf, methods (Rohlf, 1990a) is now commonly accepted: the algorithm 272 231 1990b). Both non-uniform and uniform components were used does not require the points to be equidistant (as in eigenshape 273 232 as shape variables. The shape variables were produced separately analysis (Lohmann, 1983)), and the use of the normalized coeffi- 274 233 for males and females. These variables – also called ‘‘partial warps’’ cients ‘‘saves the investigator from having to worry about aligning 275 234 (Slice et al., 1996) – actually describe the differences in shape as the images in a standard fashion’’ (Rohlf and Archie, 1984). 276 235 deviations from an average configuration of landmarks, and their 236 principal components or a subset of them (see Section 2.2.3) were 2.2.3. Assigning individuals to their corresponding groups 277 237 used as input for the discriminant analysis. Each individual was classified after a discriminant analysis of 278 shape variables according to a validated classification procedure, 279 238 2.2.2. Outline-based analyzes also called the jackknife classification (Manly, 2004), where each 280 239 For each organism, except for the ticks, a previous study was individual is allocated to its closest group without being used to 281 240 necessary to select the outline to be used in the comparisons. Var- help determine a group center. 282 241 ious outlines are possible on the same wing, and their discriminat- Because of statistical constraints (Sheets et al., 2006), the dis- 283 242 ing power can be different (Francoy et al., 2012). We tried to select criminant analysis did not use the original shape variables as input. 284 243 outlines containing the fewest possible landmarks, since the justi- The number of shape variables produced by a two-dimensional 285 244 fication for using the outline-based approach is just that: no land- landmark approach is two times the number of landmarks less four 286 245 marks, or not enough of them. In each comparison, we used the (Rohlf, 1996), and can be larger than the number of individuals. To 287 246 elliptic Fourier analysis (EFA) (Kuhl and Giardina, 1982). Briefly, circumvent the problem of high-dimensionality in the data set, a 288
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Fig. 4. Top, left: Outline as digitized for Ornithodoros maritimus. Top, right: 15 landmarks on the ventral face (red dots). Bottom: Riou and Rouzic localities have different outlines for Ornithodoros maritimus.
289 few first principal components (PC) of shape variables were used as and is contained in a single variable: the root-squared area of a 315 290 input for the discriminant analysis and subsequent validated clas- contour (instead of its perimeter (Lestrel, 1997)), or the ‘‘centroid 316 291 sification. The size of this subset of PC was simply the number of size’’ for a configuration of landmarks (Bookstein, 1991). The cen- 317 292 specimens of the smallest group less one. There are other, more troid size is defined as the square root of the sum of the squared 318 293 sophisticated ways to select for an adequate morphological sub- distances between the center of the configuration of landmarks 319 294 space, but most rules of selection are heuristic or ad hoc methods and each separate landmark (Bookstein, 1991). The root-squared 320 295 (Jolliffe, 1986; Krzanowski, 1987). One of the least subjective is to area of a contour was actually the one of the starting ellipse in 321 296 select the number of retained components in each analysis that the Fourier decomposition (Rohlf, 1990a). 322 297 minimize the total cross-validated misclassification percentages The estimate of size was then used to evaluate its contribution 323 298 (Baylac et al., 2003; Baylac and Frieß, 2005; Sheets et al., 2006). to species discrimination based on shape: in each specific study, 324 299 We used here a much simpler approach by placing the two mor- the coefficient of determination was computed after regressing 325 300 phometric techniques in similar statistical conditions, even if these the first (and generally unique) discriminant function on size. 326 301 conditions were not optimal for obtaining the best scores. 302 The use of the outline method also poses difficulties for the dis- 303 criminant analysis. To accurately represent a curve many harmon- 2.2.5. Measurement error 327 304 ics may be needed, each with four coefficients, so that the analyzes The precision of both techniques was estimated using the 328 305 would require the measurement of numerous samples (Iwata, repeatability index. For each sample, we used an ANOVA design 329 306 2011). In each of our examples, the PC of the (normalized) Fourier on repeated measurement performed on the totality the sample. 330 307 coefficients were not derived from the total number of coefficients The repeatability was measured using different variance compo- 331 308 because it was frequently larger than the total sample. Thus, only a nents: the among-measurement (Va) and the within-measurement 332 309 part of the Fourier coefficients was submitted to the PCA, and only (Vw) components. It was expressed in percentage, and computed 333 310 a part of the first PC was used as input for the DA, where the same as the ratio of Va/(Va + Vw) (Arnqvist and Mårtensson, 1998), 334 311 rule as above (‘‘the smallest group less one’’) was adopted. which is the reverse of the measurement error as suggested by 335 Yezerinac et al. (1992). The input variables for the ANOVA were 336 312 2.2.4. Size estimation and its contribution to shape discrimination the four first principal components of shape. We only report the 337 313 Whichever way shape is computed, the estimate of size in mod- average and standard deviation of the repeatability across species 338 314 ern morphometrics is restricted to the geometric area examined and comparisons for each morphometric approach. 339
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340 2.3. Software showed a very close outline in spite of different habitats (Fig. 1) 367 and very different sizes (Fig. 5). 368 341 All analyzes and graphical output were obtained through the 342 various modules of the CLIC package (Dujardin and Slice, 2006; 3.1.2. G. p. palpalis and G. p. gambiensis 369 343 Dujardin et al., 2010): the COO module to digitize either landmarks There was a notable difference in the morphometric discrimina- 370 344 or curves, the MOG and PAD modules for the landmark-based tion of subspecies according to the sex. Both morphometric tech- 371 345 approach, the FOG module for EFA analyzes and subsequent vali- niques produced comparable and unsatisfactory results for male 372 346 dated classifications, and the VAR module for estimating the specimens (67–70% versus 66–77%, for landmarks and outlines, 373 347 repeatability of size and shape. respectively). For females, only the outline comparisons produced 374 satisfactory results (80–85%, versus 70–73% for landmark compar- 375 isons). Thus, outlines could only discriminate the two forms for 376 348 3. Results females. 377
349 The quality of group discrimination was generally satisfactory 350 in both methods (Table 1, see the ‘‘%’’ columns) (86% of correct 3.1.3. A. strodei and A. oswaldoi 378 351 assignation on average versus 78% for landmarks). On average, In this simple pairwise comparison of female mosquitoes, both 379 352 the repeatability was slightly higher for outlines (85 ± 11%) than landmarks and outlines performed satisfactorily (more than 83% 380 353 for landmarks (79 ± 17%). The contribution of size to the discrimi- were correctly classified). The scores obtained using landmarks 381 354 nation appears in Table 1 as a percentage in the ‘‘r2%’’ column. On (84%–86%) were slightly inferior to the ones obtained using the 382 355 average, it was lower in the landmark-based approach (23 ± 18% contour of a single internal cell of the wing (87%–94%). 383 356 versus 33 ± 23%). 3.1.4. O. maritimus 384 The geographic origin of the 39 specimens of O. maritimus was 385 357 3.1. Group distinction almost perfectly recognized by the body contour (94%–100%), 386 while imperfectly – but still consistently – by the landmark 387 358 3.1.1. R. prolixus and R. robustus approach (80%–83%). 388 359 The landmark approach produced relatively satisfactory results: 360 15 (out of 17) R. robustus were correctly assigned, while 2 (out of 361 24) R. prolixus were wrongly attributed to R. robustus. More impres- 4. Discussion 389 362 sive results were obtained from outlines, where no R. prolixus was 363 assigned to the R. robustus species, and only one R. robustus (out of For analyzing curves containing anatomical landmarks, as is the 390 364 18) was confounded with R. prolixus. The R. robustus outline was case for most of the material used in this study, and in spite of 391 365 not affected by the different geographic areas (Fig. 1) and the dif- some unresolved drawbacks (Sheets et al., 2004), the sliding 392 366 ferent sizes (Fig. 5). Similarly, the two samples of R. prolixus semi-landmark approach becomes more frequently adopted in 393
Table 1 Validated classification based on shape. ‘‘n’’, sample sizes; ‘‘%’’, percentages of correct assignation of individuals to relevant species or group. ‘‘LM’’, the number of true landmarks used; ‘‘H’’, the number of harmonics corresponding to the number of normalized Fourier coefficients used as input for the principal component analysis; ‘‘r2%’’, percent contribution of size to the first discriminant factor based on shape. The analyzes comparing R. robustus and R. prolixus were repeated with two different sampling distribution: either 14, 10 (both R. prolixus) and 18 R. robustus (mixing their geographic origins, i.e. Candelaria and Pampanito), or 14, 10, 8 R. robustus from Candelaria and 10 from Pampanito. 23/22, 30/22, 17/18 or 7/8 mean a different sample according to the approach: the first number refers to the sample size analyzed with landmarks, the second number to the one analyzed with outlines; F, females; M, males.
Species and groups n Validated classifications Landmarks Outlines LM % r2% H % r2% Tsetse flies, females 10 1 11 0 G. p. palpalis 26 73 80 G. p. gambiensis 20 70 85 Tsetse flies, males 10 2 9 2 G. p. palpalis 15 67 66 G. p. gambiensis 23/22 70 77 Mosquitoes 11 27 13 59 A. strodei 32 87 87 A. oswaldoi 30/22 93 94 Kissing bugs, 2 groups 7 45 10 41 R. prolixus (2 habitats) 24 83 100 R. robustus 17/18 94 94 Kissing bugs, 3 groups 7 39 10 40 R. prolixus (domestic) 14 86 92 R. prolixus (silvatic) 10 70 80 R. robustus 17/18 88 94 Kissing bugs, 4 groups 7 11 10 52 R. prolixus (domestic) 14 86 92 R. prolixus (silvatic) 10 70 80 R. robustus (Candelaria) 7/8 100 87 R. robustus (Pampanito) 10 77 100 Soft ticks 15 34 9 40 O. maritimus Rouzic 18 80 94 O. maritimus Riou 20 83 100
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Fig. 5. Size variation as estimated by the square root of outline areas. From top to bottom: kissing bugs (Rhodnius robustus and R. prolixus), mosquitoes (Anopheles strodei and A. oswaldoi), tsetse flies (G. p. p. = Glossina palpalis palpalis, and G. p. g. = Glossina p. gambiensis) and ticks (Ornithodoros maritimus from two localities, Riou and Rouzic).
394 arthropods (Yee et al., 2011; Palestrini et al., 2012; Stephens and to curves, closed or not (Lohmann, 1983), as reviewed by Rohlf 409 395 Juliano, 2012). The main benefit is that both landmarks and (1990a) and, more recently, by Sheets et al. (2006). The attractive 410 396 semi-landmarks can be arranged in one data set submitted to a sin- feature of the EFA algorithm, which is used here, is that points need 411 397 gle analysis. The discriminating power has been shown to be sim- not to be equidistant nor equal in number. 412 398 ilar to the elliptic Fourier analysis (EFA) and some other outline 399 methods (Sheets et al., 2006). The semi-landmark approach consid- 4.1. Data collection 413 400 ers both curves and landmarks. As long as it requires true land- 401 marks in order to describe the curves, it is a landmark-dependent In both approaches, we used manual data collection: a manual 414 402 method. The outline-based approach is completely different, as collection of landmarks, and a manual digitization of contours. For 415 403 outlines may or may not include landmarks; it is a landmark-free the landmark approach, it is not advisable to mix data from differ- 416 404 method. Thus, for corresponding structures having very few land- ent users because of the ‘‘user effect’’: small but persistent differ- 417 405 marks or simply lacking landmarks, it is advisable to turn to the ences may arise between different users in pointing to the exact 418 406 classical outline analysis based on EFA. We showed here that this location of some landmarks, with effects on the final output 419 407 option does not mean lower discriminating power. The EFA applies (Dujardin et al., 2010). Because of the high comparability of each 420 408 to closed contours only. Other statistical techniques can be applied separate landmark, it is mandatory to collect each one very care- 421
Q1 Please cite this article in press as: Dujardin, J.-P., et al. Outline-based morphometrics, an overlooked method in arthropod studies? Infect. Genet. Evol. (2014), http://dx.doi.org/10.1016/j.meegid.2014.07.035 MEEGID 2043 No. of Pages 10, Model 5G 12 August 2014
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422 fully. Since the comparability of points (pseudolandmarks) is not a Thus, poor sample sizes could have reduced the discriminating 486 423 problem in outline digitization, it might be expected that the user power of outlines relative to landmarks. In spite of that, the outline 487 424 effect is lower. However, it has been shown that different users, classification showed satisfactory results. 488 425 hence different ways to capture the contour, may have a more 426 (Firmat et al., 2010) or less (Qing-Bin and Xin-Li, 2012) significant 4.3. Compared scores 489 427 effect on the final output. We did not test for the compared magni- 428 tude of the user effect between landmark and outline techniques, The morphometric methods used here to build shape represen- 490 429 but we show that, for the same images, both techniques suffered tation remove the isometric change of size, but were not designed 491 430 from a more or less similar amount of measurement error. to remove the allometric effect of size on shape variation. Thus, 492 431 For both landmark and outline approaches, the automated cap- shape variables, be they partial warps or normalized Fourier coef- 493 432 ture of data could appear as the solution, but no such solution has ficients, contain an allometric residue. Here, the effect of this resi- 494 433 been widely adopted so far. Automated identification of landmarks due on shape discrimination was estimated by regressing the 495 434 was recently proposed for Dipteran wings (Houle et al., 2003; shape-derived discriminant factor on size variation; which is 496 435 Palaniswamy et al., 2010), but it may be a very difficult task when reported in the column ‘‘r2%’’ of Table 1. On average, it was slightly 497 436 applied to wings covered with scales (the Anopheles in this study) less present in the landmark approach, which suggests the land- 498 437 or when crosses between veins are wide zones instead of points mark-based approach might provide a slightly greater indepen- 499 438 (kissing bugs). Automated curve tracing is already part of many dence between size and shape. 500 439 imagery software and might look easier to generalize. It is a possi- 440 ble task when the contour is the external boundary of the body, as 4.3.1. R. prolixus and R. robustus 501 441 can be the case for rounded bodies as for instance in mussels Previous morphometric studies based on landmarks could dis- 502 442 (Ferson et al., 1985; Claude, 2008). The problem becomes more dif- tinguish the two species to some extent, but in the favorable situ- 503 443 ficult when the perimeter to be digitized is a substructure of the ation of allopatry (samples coming from distinct geographic areas) 504 444 main image, an internal part of the body. These and other obstacles (Matias et al., 2001; Villegas et al., 2002; Márquez et al., 2011). In 505 445 in performing automatic captures of outlines have led some our material, we compared the two species from samples collected 506 446 authors to rely on the more flexible manual collection (Sheets in the same village (Pampanito, Venezuela). 507 447 et al., 2006; Firmat et al., 2010). Morphological differences between the two taxa were fre- 508 448 TENTATIVE Even if adopting the same starting point is not man- quently thought to be related to their separate environment, 509 449 datory for the EFA method we used, we digitized the contour trying domestic for prolixus and silvatic for robustus. In our material, we 510 450 to start from the same point. This strategy, when possible, allows could compare a sample of domestic R. prolixus (Pampanito) with 511 451 other methods to be applied to the same set of pseudolandmarks, another one collected in silvatic conditions in another locality 512 452 as for instance the Procrustes superposition (after equalizing the (Guárico). In spite of coming from different habitats and localities, 513 453 number of points) used for our graphical output (Figs. 1–4). the two R. prolixus samples were similar, and remained distinct 514 from R. robustus. 515 454 4.2. Statistical analyzes Both landmarks and outlines did a relatively good job, with 516 again better results obtained from outlines. Actually, the outline- 517 455 The reclassification procedure used in the present study was based distinction was almost perfect, since only one R. robustus 518 456 based on the discriminant analysis. This analysis requires that out of 18 was confounded with R. prolixus, and no R. prolixus were 519 457 there be more specimens than variables in the smallest group. As confounded with R. robustus. Moreover, these outline-scores were 520 458 we were unable to increase the sample sizes, we used a principal not affected by subdividing the R. robustus sample according to 521 459 component analysis (PCA) to reduce the dimensionality of the its geographic origin. Within both species, the stability of the out- 522 460 data: the scores of a limited number of PC was used instead of line was remarkable: it was constant whatever the size (Fig. 5), the 523 461 the original data. The criterion to determine the number of PC habitat and the geographic origin (Table 1). 524 462 selected was simply the number of specimens in the smallest As for previous studies on these two close species, the shape dif- 525 463 group less one. Thus, in our study, the selected number of PC can ferences could not be clearly separated from the size variation (see 526 464 be deduced from Table 1 by looking at the sample sizes in each the column ‘‘r2%’’ of Table 1). Feliciangeli et al. (2007) showed that 527 465 comparison (see the ‘‘n’’ column). More sophisticated procedures in silvatic conditions size variance of R. prolixus may be signifi- 528 466 exist (Baylac and Frieß, 2005), some of them not based on the cantly smaller. Size is generally considered more evolutionarily 529 467 PCA method (Sheets et al., 2006). labile than shape, but it seems to have played a major role in the 530 468 Because of the PCA-based dimension reduction method, only a evolutionary divergence between R. prolixus and R. robustus (Fig. 5). 531 469 part of shape variation, although generally a consistent part, was 470 used to classify individuals, which probably lowered the power 4.3.2. Tsetse flies 532 471 of the analyzes. There are two more reasons to think that low sam- Males of G. p. palpalis and G. p. gambiensis were unsatisfactorily 533 472 ple sizes probably represented a heavier handicap for the outline discriminated by both landmarks and outlines. Since each individ- 534 473 approach than for the landmark one. First, according to Baylac ual of this study was not identified by a more accurate criterion 535 474 and Frieß (2005), the first PCA axes of Fourier coefficients rarely than its geographic origin, we cannot certify that the two groups 536 475 account for a significant proportion of the between-group differ- were completely homogeneous. However, in the case of these two 537 476 ences. Second, we were not even able to use the maximum possible subspecies, for which the taxonomic status is still discussed, the 538 477 number of harmonics because the total sample was too low for the problem concerns particularly the females. Males indeed can be 539 478 PCA to include them all. Table 1 indicates the number of harmonics recognized by observable variations of the genitalia (terminal dila- 540 479 used for the PCA (see the column ’’H’’; which can also be deduced tations of inferior clusters). Fortunately, it appears that, for females, 541 480 by computing the total sample of each group less 1, and divided by modern morphometrics can contribute to their identification, espe- 542 481 4). Between close or cryptic species, and a fortiori between conspe- cially by using the boundary of the central cell (80%–85%). 543 482 cific populations, we expect to have discrete, localized differences, 483 if any. Such differences are captured by the higher level harmonics, 4.3.3. Mosquitoes 544 484 while the first few ones generally describe more global differences Mosquitoes represent a group of insects where very few out- 545 485 in shape (Lestrel, 1989). line-based morphometric analyses have been published (Garros 546
Q1 Please cite this article in press as: Dujardin, J.-P., et al. Outline-based morphometrics, an overlooked method in arthropod studies? Infect. Genet. Evol. (2014), http://dx.doi.org/10.1016/j.meegid.2014.07.035 MEEGID 2043 No. of Pages 10, Model 5G 12 August 2014
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547 and Dujardin, 2013). Yet the first application of EFA to the shape of The objective of this study was to illustrate the interest of a rel- 609 548 living organisms was the quantitative analysis of the boundary of atively neglected method in modern morphometrics as applied to 610 549 mosquito wings (Rohlf and Archie, 1984). The material of this sem- arthropods. We show that outline analyses can be applied in com- 611 550 inal study was a set of drawings coming from an entomological plement or as an alternative to the landmark-based approach. 612 551 book (plates 1 to 127 in Carpenter and LaCasse (1955)). As far as 552 we know, no other outline studies have been performed on mos- Acknowledgments 613 553 quitoes, except a study of the genital leaflet of C. neavei (Boussès 554 et al., 2013; Garros and Dujardin, 2013), and the use of sliding- To Fernando Monteiro for kindly providing us the wings of the 614 555 landmarks combining the posterior edge of the wing with a set Rhodnius specimens he submitted to molecular analyses (Monteiro 615 556 of true landmarks in Aedes aegypti and Aedes albopictus (Stephens et al., 2003). Thierry Boulinier and David Gremillet are thanked for 616 557 and Juliano, 2012). The outline used in our mosquito sample was help with tick collections. This work benefited from international 617 558 this posterior edge united to the anal vein of the wing (Fig. 3). None collaboration through the ECLAT and LTTRN networks. 618 559 of the landmarks used for the landmark-based approach was 560 included in this contour, but it could still discriminate the two 561 close species. References 619
Adams, D.C., 1999. Methods for shape analysis of landmark data from articulated 620 562 4.3.4. Soft ticks structures. Evol. Ecol. Res. 1 (8), 959–970. 621 Albutra, Q.B., Torres, M.A.J., Demayo, C.G., 2012. Outline and landmark based 622 563 Fifteen landmarks of the ventral face were used, all of them geometric morphometric analysis in describing sexual dimorphism in wings of 623 564 however of type II, i.e. landmarks providing less homology the white stem borer (Schirpophaga innotata walker). ABAH 4 (1), 5–14. 624 565 (Bookstein, 1991). In spite using low quality landmarks, good Arnqvist, G., Mårtensson, T., 1998. Measurement error in geometric morphometrics: 625 empirical strategies to assess and reduce its impact on measure of shape. Acta 626 566 scores were obtained, but the contour of the dorsal face of the tick Zool. Acad. Sci. Hung. 44 (1–2), 73–96. 627 567 provided even better scores. Whatever the approach, significant Baltanas, A., Alcorlo, P., Danielopol, D.L., 2002. Morphological disparity in 628 568 shape divergence was disclosed between geographic populations populations with and without sexual reproduction: a case study in Eucypris 629 630 569 of the same species. These differences could reflect adaptation to virens (Crustacea: Ostracoda). Biol. J. Linn. Soc. 75 (1), 9–19. Bargues, M.D., Schofield, C., Dujardin, J.P., 2010. The phylogeny and classification of 631 570 different hosts and/or geographic isolation (two remote islands). the triatominae. In: Telleria, J., Tibayrenc, M. (Eds.), American Trypanosomiasis: 632 571 From these initial results, we expect this approach may perform Chagas disease, One hundred years of research. 633 634 572 well in species discrimination within the O. capensis group. Baylac, M., Frieß, M., 2005. Fourier descriptors, Procrustes superimposition, and data dimensionality: an example of cranial shape analysis in modern human 635 populations. In: Modern Morphometrics in Physical Anthropology, Slice ed. p. 636 145–165 (Chapter 6). 637 573 5. Conclusion Baylac, M., Villemant, C., Simbolotti, G., 2003. Combining geometric morphometrics 638 with pattern recognition for the investigation of species complexes. Biol. J. Linn. 639 640 574 Our four case-studies do not (and cannot) claim superiority of Soc. 80 (1), 89–98. Bookstein, F.L., 1991. Morphometric Tools for Landmark Data. Geometry and 641 575 one technique over another, but do suggest that one should not Biology. Cambridge University Press, NY. 642 576 overlook the outline approach for discriminating close forms. It Bookstein, F.L., 1997. Landmark methods for forms without landmarks: localizing 643 577 produced high scores of correct group allocation, it suffered from group differences in outline shape. Med. Image Anal. 1, 225–243. 644 Bourke, B.P., Oliveira, T.P., Suesdek, L., Bergo, E.S., Sallum, M.A., 2013. A multi-locus 645 578 a relatively low measurement error and could be applied to various approach to barcoding in the Anopheles strodei subgroup (Diptera: Culicidae). 646 579 organs of the body, including or not relocatable landmarks. Our Parasites Vectors 6. http://dx.doi.org/10.1186/1756-3305-6-111. 647 580 study suggests that if the main objective is to distinguish morpho- Boussès, P., Dehecq, J.S., Brengues, C., Fontenille, D., 2013. Updated inventory of 648 mosquitoes (Diptera: Culicidae) of the island of La Réunion, Indian ocean. Bull. 649 581 logically close entities, and even if a landmark-based approach is Soc. Pathol. Exot. 106, 113–125. 650 582 possible, one should consider also to test the outline technique. Carpenter, S.J., LaCasse, W.J., 1955. Mosquitoes of North America (north of Mexico). 651 583 In some organisms other than arthropods where such compar- Univ. California Press, Los Angeles. 652 Claude, J., 2008. Morphometrics with R. Springer Science+Business Media, LLC, ISBN 653 584 isons have been performed, either the two methods were consid- 978-0-387-77789-4, p. 330. 654 585 ered as equivalent (Jensen et al., 2002) or the outlines were said Crampton, J.S., 1995. Elliptic Fourier shape analysis of fossil bivalves: some practical 655 586 to perform better (Loy et al., 2000; Pavlinov, 2000; Baylac and considerations. Lethaia 28, 179–186. 656 657 587 Frieß, 2005; Albutra et al., 2012). A recent study on Hymenoptera David, B., Dommergues, J.L., Magniez-Jannin, F., 1996. Exploration d’un espace morphologique les carabes (Coleoptera) du nord-est de la France. C. R. Acad. 658 588 wings showed that landmarks performed better than (internal Sci., Life Sci. 319, 835–842. 659 589 wing cells) outlines, and that their combination provided the best De la Rocque, S., Geoffroy, B., Michel, J.F., Borne, F., Solano, P., Meunier, J.Y., Cuisance, 660 661 590 results (Francoy et al., 2012). In our various examples involving D., 2002. Tsetse flies wings, an identity card of the insect? Parasite 9 (3), 275–281. Dietrich, M., Gómez-Daz, E., McCoy, K.D., 2011. Worldwide distribution and 662 591 arthropods, similar or even better discrimination scores were diversity of seabird ticks: implications for the ecology and epidemiology of 663 592 attained using the outline-based approach. Although our sample tick-borne pathogens. Vector-Borne Zoonotic Dis. 11 (5), 447–453. 664 593 sizes were generally low, similar performances of outlines in four Dietrich, M., Lebarbenchon, C., Jaeger, A., Le Rouzic, C., Bastien, M., Lagadec, E., 665 McCoy, K.D., Pascalis, H., Le Corre, M., Dellagi, K., Tortosa, P., in press. Rickettsia 666 594 comparisons involving quite different organisms (ticks, tsetse flies, species in seabird ticks of the Southwestern Indian Ocean: distribution and 667 595 mosquitoes and kissing bugs) is a significant signal. It suggests that investigation of host specificity. 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