DOI: 10.5061/dryad.v41ns1s5d
title: Comparative shape of two recently diverged species of Pacific Rockfish: Sebastes ciliatus and S. variabilis

description: Species delimitation can be based on consideration of several different
 criteria, including differentiation of ecological or functional traits.
 Two species of Pacific rockfish, the dark rockfish (Sebastes ciliatus) and
 the dusky rockfish (Sebastes variabilis), appear to represent recently
 divergent evolutionary lineages. We evaluate evidence for differentiation
 of these two species in somatic shape using geometric morphometrics in two
 locations in the northeast Pacific where they occur in sympatry. Somatic
 shape was significantly different between species, but species’ shape did
 not vary between the two locations. Sebastes ciliatus had an upturned,
 relatively smaller head, eye, and jaw, and an elongated mid-body; whereas,
 S. variabilis had a downturned, larger head, eye, and jaw, and a shorter
 mid-body. These results suggest that S. ciliatus and S. variabilis are
 morphometrically differentiated in a similar way in both locations.
 Somatic shape differentiation between these two sympatric species is
 similar to genus-wide patterns of somatic shape differentiation.
description: For this study, we collected <em>S. ciliatus</em>
 and<em> S. variabilis </em>via hook and line fishing in
 Frederick Sound near the north end of Kuiu Island, Alaska in late June of
 2016, and in Icy Strait, Alaska in June of 2023 (160 km straight line
 distance between the two sampling locations; Figure 1). We used samples of
 both species collected in both locations for comparison of divergence in
 somatic shape. We used a total of 105 specimens; 60 <em>S.
 ciliatus</em>, 34 from Kuiu (18 males, 16 females) and 26 from Icy
 Strait (15 males, 11 females); 45 <em>S. variabilis</em>, 17
 from Kuiu (8 males, 9 females) and 28 from Icy Strait (12 males, 16
 females). Both species are considered sexually monomorphic and we had a
 relatively even mix of both sexes in each sample. Specimens for both
 species ranged from 30 – 47 cm in both locations.
 Specimens were photographed within a few minutes after capture to
 ensure that photos reflected fresh, relaxed, and natural shape variation.
 For each individual in all samples we photographed the right lateral view
 for shape analysis, and we digitized 13 landmarks (Figure 2) using tpsDig.
 Landmarks were (1) anterior tip of the lower jaw (i.e., symphyseal knob);
 (2) anterior extent of the eye; (3) posterior extent of the eye; (4)
 posterior extent of the operculum; (5) posterior, ventral extent of the
 maxilla; (6) anterior insertion of the dorsal fin on body outline; (7)
 posterior extent of scalation at the midline on the caudal peduncle; (8)
 anterior insertion of anal fin; (9) anterior insertion of pelvic fin; (10)
 dorsal outline vertical of landmark 3; (11) dorsal outline halfway between
 landmark 6 and 7; (12) dorsal outline at smallest width of caudal
 peduncle; (13) ventral outline at smallest width of caudal peduncle.
 Landmarks 2, 3, 11, 12, and 13 are sliding semilandmarks. All specimens
 were landmarked by one researcher without respect to predictor variables.
 The landmarked images were then independently inspected by another
 researcher to confirm homologous and consistent placement of landmarks.
 This procedure results in reduced error in digitizing. We used tpsRelW to
 align specimens via a generalized Procrustes analysis (a superimposition
 method to remove non-shape variation via rotation, translation, and
 scaling). We generated shape variables as partial warps and uniform
 components comprising the weight matrix
 (i.e., <strong>W</strong>). The weight matrix is used as input
 for a principal component analysis and the resulting principal components,
 termed relative warps [29], are used as shape variables for the subsequent
 analysis. Like all principal components, relative warps are ordered by the
 amount of variation they individually explain. Typically, we use all
 relative warps that account for &gt;1% of variation. In this study, we
 used the first 10 relative warps (that collectively explain 97.1% of shape
 variation) to characterize shape. We used a
 multivariate linear mixed model to determine if shape differed between
 <em>S. ciliatus</em> and <em>S. variabilis</em> or
 between locations. The response variable was shape as represented by the
 first 10 relative warps. The predictor variables were species
 (<em>S. ciliatus</em> or <em>S. variabilis</em>),
 location (Kuiu or Icy Strait), centroid size (a multivariate measure of
 size), and the index variable (indexing relative warps 1-10, see
 explanation below). We also included all two-way and three-way
 interactions with the index variable and other predictor variables. Body
 size is a common influence on shape among many species of fishes. Although
 our samples varied little in size, we included centroid size as a
 covariate to avoid confounding shape caused by size with shape differences
 between species because of potential sampling differences.
 A multivariate linear mixed model assumes a univariate response
 variable, so we vectorized the shape variables such that each row
 represented one response variable, but each specimen was represented by
 multiple rows of data. Thus, the first row represented relative warp 1 for
 the first specimen, the second row represented relative warp 2 for the
 first specimen, and so forth until all relative warps were represented in
 successive rows for the first individual. The same pattern was repeated
 for all individuals, each with 10 rows. The index variable preserved the
 order of the relative warps such that comparisons between groups (e.g.,
 <em>S. ciliatus</em> and <em>S. variabilis</em>)
 were made by matching each relative warp to the same relative warp in each
 group (e.g., relative warp 1 in one species was compared to relative warp
 1 in the other species). Our main goal was to determine how shape differed
 between species and if shape was different within species in different
 locations; thus, it is the two-way interaction of species and the index
 variable and the three-way interaction of species and location and index
 variable that tested our hypothesis of interest. Main effects by
 themselves (without the interaction with the index variable) test only for
 an average effect across all relative warps. Because relative warps are
 principal components, they have a mean of 0; and more importantly, they
 have an arbitrary ordination. Thus, a single individual may have a
 positive score on some relative warps and a negative score on other
 relative warps so that their mean score across all relative warps may be
 near 0. It was only by matching relative warps in the same order (by using
 the index variable as a predictor) that we could accurately test the
 hypothesis of interest. Specifically, the hypothesis of interest is: does
 shape differ between species or locations on at least some relative warps
 (i.e., shape variables)? This vectorization of the multivariate response
 variables allows parametric testing of multiple and complex interaction
 effects and has been used successfully to test for shape variation in a
 variety of systems, among populations, and species. We estimated degrees
 of freedom using the Kenward and Roger method. We used Proc MIXED in SAS
 to run this analysis (SAS version 9.4; SAS Institute Inc., Cary, NC,
 USA). To visualize the differences between species we
 plotted mean scores by species for each of the 10 relative warps (i.e.,
 shape variables). In addition, we calculated a divergence vector between
 <em>S. ciliatus</em> and <em>S. variabilis</em>
 across all 10 relative warps using methods from Langerhans and Langerhans
 and Makowicz. To create the divergence vector, we performed a principal
 components analysis (PCA) of the least squares means for each species
 derived from the multivariate linear mixed model output. We multiplied
 values of the first eigenvector (from the PCA) by each of the
 corresponding relative warp values for each individual and then summed
 these to create the individual divergence score. We then used the vector
 of divergence scores as the regressor variable in tpsRegr and the original
 tps landmark file as the response variable to generate thin-plate spline
 visualizations of species’ shapes. These visualizations represent overall
 shape divergence between species across all 10 relative warps
 simultaneously.
description: # Comparative shape of two recently diverged species of Pacific Rockfish:
 Sebastes ciliatus and S. variabilis
 [https://doi.org/10.5061/dryad.v41ns1s5d](https://doi.org/10.5061/dryad.v41ns1s5d) ## Description of the data and file structure For this study, we collected *S. ciliatus* and *S. variabilis* via hook and line fishing in Frederick Sound near the north end of Kuiu Island, Alaska in late June of 2016, and in Icy Strait, Alaska in June of 2023 (160 km straight line distance between the two sampling locations; Figure 1). We used samples of both species collected in both locations for comparison of divergence in somatic shape. We used a total of 105 specimens; 60 *S. ciliatus*, 34 from Kuiu (18 males, 16 females) and 26 from Icy Strait (15 males, 11 females); 45 *S. variabilis*, 17 from Kuiu (8 males, 9 females) and 28 from Icy Strait (12 males, 16 females). Both species are considered sexually monomorphic  and we had a relatively even mix of both sexes in each sample. Specimens for both species ranged from 30 – 47 cm in both locations. ### Files and variables #### File: cilvar\_data\_for\_Dryad.xlsx **Description:** excel file used as input for the linear mixed model analysis of shape. ##### Variables | Variable | Description | | :------- | :------------------------------------------------------------------------------------------------------------------ | | ID | Unique individual identifier | | Species | C stands for S. ciliatus and V stands for S. variabilis | | loc | Location of collection, Kuiu is labeled as location 2 on the map and Icy Strait is labeled as location 1 on the map | | cs | centroid size is a multivariate measure of size derived from the Procrustes superimposition in tps RelW | | rw1 | relative warp 1 of 10 used as shape variables, and response variables in the model | | rw2 | relative warp 2 of 10 used as shape variables, and response variables in the model | | rw3 | relative warp 3 of 10 used as shape variables, and response variables in the model | | rw4 | relative warp 4 of 10 used as shape variables, and response variables in the model | | rw5 | relative warp 5 of 10 used as shape variables, and response variables in the model | | rw6 | relative warp 6 of 10 used as shape variables, and response variables in the model | | rw7 | relative warp 7 of 10 used as shape variables, and response variables in the model | | rw8 | relative warp 8 of 10 used as shape variables, and response variables in the model | | rw9 | relative warp 9 of 10 used as shape variables, and response variables in the model | | rw10 | relative warp 10 of 10 used as shape variables, and response variables in the model |