DOI: 10.5061/dryad.jwstqjqnb
title: Data from: Converging or diverging? Shape coevolution between a sperm-dependent asexual and its sexual hosts

description: Asexual species, despite lacking recombination, can evolve in response to
 environmental changes and influence the evolutionary trajectory of
 coexisting sexual species. Gynogenesis, where asexual females rely on
 sperm from males of a different species, offers a unique perspective on
 the eco-evolutionary dynamics between asexual females and their sexual
 hosts. The Amazon molly, Poecilia formosa, is a gynogenetic species that
 primarily uses sperm from two sympatric sexual species: the Sailfin molly
 (P. latipinna) and the Atlantic molly (P. mexicana). We analyzed shape
 variation among wild P. formosa, P. latipinna, and P. mexicana females to
 understand Amazon molly shape variation relative to their sexual hosts. We
 tested three hypotheses: (i) Amazon mollies mimic their sexual hosts to
 enhance mating opportunities (sexual mimicry hypothesis); (ii) ecological
 interactions or male mate choice drive morphological divergence (character
 displacement hypothesis); and (iii) Amazon mollies exhibit random shape
 variation due to their asexual nature (null hypothesis). Our findings
 revealed significant shape variation in Amazon mollies, which differ from
 their sexual hosts in a host-specific manner (e.g., Amazon mollies with P.
 latipinna resemble P. mexicana and vice versa), supporting character
 displacement at the interspecific level in a sexual-asexual system.
description: <em>Sampling</em> To address these
 questions, we leveraged two independent and complementary datasets that
 used slightly different methodological approaches. The first dataset
 included a balanced sampling of <em>P. formosa</em> (N=212)
 and its host species from six natural populations in Texas, USA and
 Tamaulipas, Mexico, and one common-garden-reared population (since 1994)
 from a site where <em>P. formosa</em> coexists with both host
 species. In this first dataset (Dataset I), geometric morphometric
 analyses were based on external landmarks on lateral photographs of live
 specimens taken in the field (Figure 2a). The second dataset (Dataset II)
 included a larger sample size in terms of the number of sites (N=18) and
 specimens (N=678). Despite a larger sample size, Dataset II is often
 unbalanced in terms of species sampled within collection locations
 (reflecting the relative frequency of species at the time of collection).
 For Dataset II, specimens were fixed upon collection, and geometric
 morphometric analyses were based on internal landmarks on lateral
 radiographs of the preserved specimens (Figure S2.2). All females included
 in each of the two datasets were sexually mature (&gt;30mm). Both
 approaches yielded qualitatively remarkably similar results. Given the
 more balanced distribution of specimens across sample sites in Dataset I,
 we present the details of the methods and findings based on Dataset I. The
 methods and results for the Dataset II are presented in Supplementary
 Material 2. <em>Geometric morphometric data
 processing </em> For Dataset I, sampling was
 conducted in September 2001 throughout Texas, USA and Tamaulipas Mexico,
 using a 6.4 mm mesh, 7.6 m x 1.2m standard minnow seine. The fish were
 anesthetized using Tricaine methanesulfonate, (MS-222) and photographed
 on-site. At some sites, Amazon mollies lived syntopically with
 <em>P. mexicana</em>; at others, they coexisted with
 <em>P. latipinna</em> (Figure S1; Table S1).
 We acquired images from 212 individuals (120 <em>P.
 formosa</em> females, 102 from wild populations, and 18 from one
 stock population (IV/5); 58 wild <em>P. latipinna</em>
 females, and 34 wild <em>P. mexicana</em> females) from seven
 localities across their natural range and different combinations between
 Amazon mollies and its sexual hosts (Figure S1; Table S1). We classified
 sampling points by the presence of Amazon mollies and their syntopy with
 its sexual hosts, either <em>P. latipinna</em> mainly in
 Texas, USA or <em>P. mexicana</em>, from Tamaulipas, Mexico.
 Digital photographs were taken on-site on a laminated millimeter grid
 using an Olympus Camedia 2500L digital camera. Camera set up, light
 condition, and individual positioning (left side of the individual
 positioned at the center of the laminated grid) were standardized as much
 as possible to avoid photographic errors across sampling points. To have a
 shape representation from <em>P. formosa</em> females from
 sites where Amazon mollies coexist with both<em> P.
 latipinna</em> and <em>P. mexicana, </em>we added one
 population from a laboratory stock (Figure S1; Table S1). All photos were
 taken with a Nikon D5200 camera with a Nikon with a Nikon DX 18-55mm lens
 but otherwise treated the same way as the field photos.
 Twelve landmarks and 22 semi-landmarks (Figure 2a) were digitized
 using TPSDig v. 2.30 (Rohlf,
 2015). The software TPSUtil v.1.82 (Rohlf,
 2015) was used to compile and convert image files into a ‘.tps’
 file for downstream analysis in MorphoJ v. 1.07a (Klingenberg, 2011). In MorphoJ, we superimposed the landmark configurations using generalized Procrustes analysis (Rohlf & Slice, 1990), generated a covariance matrix, and performed all geometric morphometric analyses. We conducted a multivariate regression of Procrustes coordinates (shape) and the centroid size (size) to account for allometric effects on shape variation (Klingenberg & Marugán-Lobón, 2013). We then used the residuals of this regression as size-corrected shape data for the subsequent analysis using MorphoJ. <em>Geometric morphometric analysis</em> All geometric morphometric analysis was performed using MorphoJ v. 1.07a. We first used Principal Component Analysis (PCA) to characterize the overall shape differences between females of <em>P. latipinna</em> (sexual host), <em>P. mexicana</em> (sexual host), and <em>P. formosa</em> (asexual sperm-parasite). For PCA, we selected the minimum number of principal axes that together explained at least 75% of the overall shape variation (Figure S3). We investigated whether Amazon mollies have a shape similar (sexual mimicry hypothesis), dissimilar (character displacement hypothesis), or had little/random morphological variation (null hypothesis) in relation to the sexual hosts with whom they coexist. We compared shape differences amongst these groups using a Canonical Variate Analysis (CVA) to determine the effectiveness of variation in predicting <em>a priori </em>group assignments using a T<sup>2</sup> Hotelling test with 1000 permutations with Bonferroni multiple correction. This approach tests against the null hypothesis that group averages have no shape differences. A Procrustres pairwise distance matrix was used as a shape distance matrix among groups. For individual pair-to-pair comparisons, we used Discriminant Function Analysis (DFA). We performed cross-validation and permutation tests (1000 permutations each) for all comparisons between groups to statistically evaluate the discriminant scores, using Procrustres distances. From those permutation tests, an individual classification/misclassification table was generated to assess the percentage of individuals per group correctly assigned by the DFA after 1000 permutations. We reran a CVA as described above but with a dataset containing only Amazon mollies to check for shape differences amongst Amazon mollies according to their syntopic sexual hosts. A warped outline drawing graphic was chosen to visualize shape variation.
description: # DATA FOR Converging or diverging? Shape coevolution between a
 sperm-dependent asexual and its sexual hosts
 [https://doi.org/10.5061/dryad.4f4qrfjpz](https://doi.org/10.5061/dryad.4f4qrfjpz) ## Description of the data and file structure This README file refers to the dataset used in the study "**Converging or diverging? Shape coevolution between a sperm-dependent asexual and its sexual hosts.**"  The study used two different geometric morphometrics  datasets. Therefore each dataset contains geometric morphometric files as follows: * One ".TPS" file per dataset ("AmazonMollyVariation_Dataset01.TPS" and "AmazonMollyVariation_Dataset02.TPS") containing the landmark configuration for every individual used in the study. * One "Outline file" per dataset ("AmazonMollyVariation_OutlineFile_Dataset01.txt" and "AmazonMollyVariation_OutlineFile_Dataset02.txt") containing the outline curves used to visualize the shape variation in both datasets. * One "Classifier file" per dataset ("AmazonMollyVariation_Classifiers_Dataset01_WM.csv" and AmazonMollyVariation_Classifiers_Dataset02_WM.txt") containing the classifier variables used in our analyses. * One "Dataset01_OriginalPhotos.zip" containing the original photos used in our analyses for Dataset01. For access to the original photos used in Dataset02, please contact Michi Tobler at tobler@umsl.edu ### Files and variables #### File: AmazonMollyVariation\_Classifiers\_Dataset01\_WM.csv **Description:** Classifiers variable for Dataset 01. ##### Variables * ID: The individual fish ID * Photo ID: The individual photo ID * Species: The species that the particular individual belongs.  * Pop: A general classification for the population to which the individual belongs. We classified sampling points by the presence of Amazon mollies and their syntopy with its sexual hosts, either *P. latipinna* mainly in Texas, USA or *P. mexicana*, from Tamaulipas, Mexico. Amazon mollies that coexist exclusively with *P. latipinna* (COM, WES, and LPK in Figure S1) were classified as “Pfor_Lat”. Amazon mollies that exclusively coexist with *P. mexicana* (locations BT, NP, and CC in Figure S1) were classified as “Pfor_Mex”. A single location where Amazon mollies coexist with both *P. latipinna* and *P. mexicana* was labeled as “Pfor_Both”. Females from the sexual hosts across all locations were grouped as either “Plat,” representing *P. latipinna*, or “Pmex,” representing *P. mexicana*.  * SubPop: The particular sampling point the individual came from * Month: The month it was collected * Year: The year it was collected * Preg (Y/N): A general classification whether the female was pregnant or not based on visual inpsection only. * SL: Standard length in milimiters for the particular individual. #### File: AmazonMollyVariation\_Classifiers\_Dataset02\_WM.txt **Description:** Classifiers variable for Dataset 02. ##### Variables * id: Photo and individual ID. * site: Sampling location * species: The species that the particular individual belongs.  * Pop:   A general classification for the population to which the individual belongs. We classified sampling points by the presence of Amazon mollies and their syntopy with its sexual hosts, either *P. latipinna* or *P. mexicana* Amazon mollies that coexist exclusively with *P. latipinna* were classified as “Pfor_Lat”. Amazon mollies that exclusively coexist with *P. mexicana*  were classified as “Pfor_Mex”.Females from the sexual hosts across all locations were grouped as either “Plat,” representing *P. latipinna*, or “Pmex,” representing *P. mexicana*.  #### File: AmazonMollyVariation\_OutlineFile\_Dataset01.txt **Description:** Outilne file used to visualize shape variation for dataset 01. #### File: AmazonMollyVariation\_OutlineFile\_Dataset02.txt **Description:** Outilne file used to visualize shape variation for dataset 02. #### File: AmazonMollyVariation\_Dataset01.TPS **Description:**  .TPS file used as input for analysis in MorphoJ for dataset 01. #### File: AmazonMollyVariation\_Dataset02.TPS **Description:** .TPS file used as input for analysis in MorphoJ for dataset 01. #### File: Dataset01\_OriginalPhotos.zip **Description:** Photos used in dataset 01. #### File: Dataset02\_OriginalPhotos.zip **Description:** X-Ray Photos used in dataset 02. ## Code/software  Twelve landmarks and 22 semi-landmarks (full description of landmarking in Figure S2) were digitized using TPSDig v. 2.30 [(Rohlf, 2015)](https://www.zotero.org/google-docs/?Rh2re1). The software TPSUtil v.1.82 [(Rohlf, 2015)](https://www.zotero.org/google-docs/?PvbL5E) was used to compile and convert image files into a‘.tps’ files were  for downstream analysis in MorphoJ v. 1.07a [(Klingenberg, 2011)](https://www.zotero.org/google-docs/?oE4FkI). In MorphoJ, we superimposed the landmark configurations using generalized Procrustes analysis [(Rohlf & Slice, 1990)](https://www.zotero.org/google-docs/?LctHqe), generated a covariance matrix, and performed all geometric morphometric analyses. We conducted a multivariate regression of Procrustes coordinates (shape) and the centroid size (size) to account for allometric effects on shape variation. We then used the residuals of this regression as size-corrected shape data for the subsequent analysis using MorphoJ. All analyses are fully described in the main manuscript.