1 PLoS ONE 2013 Vol: 8(3):. DOI: 10.1371/journal.pone.0057592

Host-Specific Phenotypic Plasticity of the Turtle Barnacle Chelonibia testudinaria: A Widespread Generalist Rather than a Specialist

Turtle barnacles are common epibionts on marine organisms. Chelonibia testudinaria is specific on marine turtles whereas C. patula is a host generalist, but rarely found on turtles. It has been questioned why C. patula, being abundant on a variety of live substrata, is almost absent from turtles. We evaluated the genetic (mitochondrial COI, 16S and 12S rRNA, and amplified fragment length polymorphism (AFLP)) and morphological differentiation of C. testudinaia and C. patula from different hosts, to determine the mode of adaptation exhibited by Chelonibia species on different hosts. The two taxa demonstrate clear differences in shell morphology and length of 4–6th cirri, but very similar in arthropodal characters. Moreover, we detected no genetic differentiation in mitochondrial DNA and AFLP analyses. Outlier detection infers insignificant selection across loci investigated. Based on combined morphological and molecular evidence, we proposed that C. testudinaria and C. patula are conspecific, and the two morphs with contrasting shell morphologies and cirral length found on different host are predominantly shaped by developmental plasticity in response to environmental setting on different hosts. Chelonibia testudinaria is, thus, a successful general epibiotic fouler and the phenotypic responses postulated can increase the fitness of the animals when they attach on hosts with contrasting life-styles.

Mentions
Figures
Figure 1: A. Chelonibia patula is commonly epibiotic on crustaceans surfaces. B., C. Chelonibia testudinaria (indicated by black arrow in C) is common on shell surface of marine turtles. D. Sampling locations of C. patula (squares) and C. testudinaria (circle) in the present study. E. Shell parameters measured for morphological analysis. SL – shell length, OL – orifice length, SH – shell height, ST – shell thickness. F. Cirrus IV of C. testudinaria, showing the length measured for morphological analysis. Cirri V and VI not shown due to similarity in morphology. G. Chelonibia patula, showing the small dwarf males (indicated by arrows) settled randomly on shell surface and orifice opening. H. Chelonibia testudinaria, showing the dwarf males (indicated by arrows) settled on the oval depression in the radii of the shell plates. Figure 2: A. nMDS plots showing ordinations of the morphological characters of Chelonibia patula and C. testudinaria. B. nMDS plots showing the AFLP analysis of C. patula and C. testudinaria. Refer to table 2 for the acronyms of the population. C. Mean (± SD), of the shell parameters and cirrus length (relative to the total shell length) of C. patula and C. testudinaria. Figure 3: Scanning electron microscopy of mouth parts and cirri of Chelonibia testudinaria.            A. Maxilla, B. Serrulate setae on maxilla. C. Maxillule, D. Serrulate setae on maxillule. E. Setae on cutting edge of maxillule. F. Mandibles, G. 3rd –5th teeth and the lower margin of mandible. H. Mandibular palp, showing simple type setae on the inferior margin (I, J) and serrulate setae on superior margin (K). L. Labrum showing enlarged view of teeth on cutting edge (M). N. cirrus I, showing densely pectinated serrulate setae (O) and serrulate setae on rami (P, Q). R. Cirrus II, with serrulate setae (T) and pappose setae (S). U. Cirrus III, with serrulate type setae (V, W) on rami. X. Cirrus VI, showing the intermediate segment (Y). Figure 4: Maximum likelihood (ML) tree of Chelonibia testudinaria and C. patula in this study with sequences of C. testudinaria from Rawson et al. (2003) (GenBank accession nos.: AY174312–16, 24–28, 34–8, 42–46, 58–62) and outgroup C. caretta (FJ385728-30).            ML and Bayesian inference (BI) analyses yield the same topology. Bootstrap (1,000 replicates) values for ML (normal) and the posterior probabilities for BI (bold) analyses are indicated at the nodes. (EP: eastern Pacific; WP: western Pacific; A: Atlantic). Figure 5: TCS network of Chelonibia testudinaria (white portion) and C. patula (black and hatched portions) based on mitochondrial COI, 12S and 16S rRNA markers.            Locality of populations of C. patula is indicated in respective black and hatched portions. Figure 6: FST value for each of the AFLP loci and their associated posterior odds (PO).            Solid vertical line represents the threshold value (false discovery rate of 0.05) of PO; loci with PO larger than the threshold regarded as outliers. Note that PO is equivalent to Bayes Factors when the prior odds are set to 1 (refer to text for details).
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    • . . . Sequences obtained (refer to Appendix S1 for GenBank accession nos.) were visually edited and aligned using MEGA version 4 [56] . . .
  57. Stamatakis A, Ott M, Ludwig T (2005) RAxML-OMP: An efficient program for phylogenetic inference on SMPs: Springer-Verlag , .
    • . . . To compare the COI sequences obtained from this study with those of Chelonibia testudinaria from the world’s oceans reported by [29], a maximum likelihood (ML) tree was constructed using RAxML-HPC Blackbox [57] through the online server Cyberinfrastructure for Phylogenetic Research (CIPRES; http://www.phylo.org; conducted on 9 Mar. 2011), while a Bayesian inference (BI) tree was inferred by the program MrBayes v.3.12 [58] . . .
  58. F Ronquist; JP Huelsenbeck MrBayes 3: Bayesian phylogenetic inference under mixed models Bioinformatics 19, 1572-1574 (2003) .
    • . . . To compare the COI sequences obtained from this study with those of Chelonibia testudinaria from the world’s oceans reported by [29], a maximum likelihood (ML) tree was constructed using RAxML-HPC Blackbox [57] through the online server Cyberinfrastructure for Phylogenetic Research (CIPRES; http://www.phylo.org; conducted on 9 Mar. 2011), while a Bayesian inference (BI) tree was inferred by the program MrBayes v.3.12 [58] . . .
  59. D Posada jModelTest: Phylogenetic model averaging Mol Biol Evol 25, 1253-1256 (2008) .
    • . . . Transversional model (TVM) with proportion of invariable site (+I) and rate variation among site (+G) was estimated to be the optimal substitution model from Akaike information criterion by jModelTest version 0.1.1 [59] . . .
  60. M Clement; D Posada; KA Crandall TCS: a computer program to estimate gene genealogies Mol Ecol 9, 1657-1659 (2000) .
    • . . . To infer the intraspecific population structure, haplotype networks of the three mitochondrial markers were generated by TCS version1.13 [60] . . .
  61. M Kimura A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences J Mol Evol 16, 111-120 (1980) .
    • . . . The haplotype and nucleotide diversities, as well as the Kimura’s two-parameter (K2P) distance [61], were calculated for the three markers using Arlequin version 3.1 [62] and MEGA, respectively . . .
  62. L Excoffier; G Laval; S Schneider Arlequin ver. 3.0: An integrated software package for population genetics data analysis Evol Bioinform 1, 47-50 (2005) .
    • . . . The haplotype and nucleotide diversities, as well as the Kimura’s two-parameter (K2P) distance [61], were calculated for the three markers using Arlequin version 3.1 [62] and MEGA, respectively . . .
  63. A Bonin; D Ehrich; S Manel Statistical analysis of amplified fragment length polymorphism data: a toolbox for molecular ecologists and evolutionists Mol Ecol 16, 3737-3758 (2007) .
    • . . . As recommended by [63] to have >5–10% subsamples to calculate the genotyping error rate [64], over 26% of samples (20 out of 76) was haphazardly chosen and genotyped twice in the present study . . .
  64. F Pompanon; A Bonin; E Bellemain; P Taberlet Genotyping errors: causes, consequences and solutions Nat Rev Genet 6, 847-859 (2005) .
    • . . . As recommended by [63] to have >5–10% subsamples to calculate the genotyping error rate [64], over 26% of samples (20 out of 76) was haphazardly chosen and genotyped twice in the present study . . .
  65. Vekemans X (2002) AFLP-SURV, Version 1.0, Laboratoire de Génétique et Ecologie Végétale. xelles, Belgium: Université Libre de Bruxelles , .
    • . . . AFLPsurv version 1.0 [65], which is an allelic-frequency-based method that utilizes the unbiased estimator of allelic frequency to assess genetic diversity (i.e. expected heterozygosity) [66], was used to estimate the pairwise relatedness coefficients between individuals of C. patula and C. testudinaria, and to test whether there is significant population differentiation between the species through 10,000 permutations, assuming Hardy-Weinberg genotypic proportion . . .
  66. M Lynch; BG Milligan Analysis of population genetic structure with RAPD markers Mol Ecol 3, 91-99 (1994) .
    • . . . AFLPsurv version 1.0 [65], which is an allelic-frequency-based method that utilizes the unbiased estimator of allelic frequency to assess genetic diversity (i.e. expected heterozygosity) [66], was used to estimate the pairwise relatedness coefficients between individuals of C. patula and C. testudinaria, and to test whether there is significant population differentiation between the species through 10,000 permutations, assuming Hardy-Weinberg genotypic proportion . . .
  67. M Foll; OE Gaggiotti A genome scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective Genetics 108, 977-993 (2008) .
    • . . . The AFLP candidate loci under selection pressure, which are the outlier loci demonstrating FST values significantly higher than overall mean values across the loci, were identified by BayeScan version 2.01 [67] . . .
    • . . . The false discovery rate (FDR) of this program was shown to be much lower than the other commonly used programs in detecting loci under selection and the multinomial Dirichlet model adopted in the program that allows for differential effective size and migration rate among populations was believed to be more ecologically realistic [67] . . .
    • . . . A locus-specific statistic, alpha, which is decomposed from the FST values that reflect the locus-wise difference of allele frequency, was used to infer the selection [67] . . .
  68. R-Development-Core-Team (2009) R: A language and environment for statistical computing. Available: Link. Accessed 2013 Feb 1 , .
    • . . . Graphical illustrations of the results were done using R-package [68] as described in the user manual [69]. . . .
  69. Foll M (2010) BayeScan v2.0 user manual. Switzerland: Bern , .
    • . . . Graphical illustrations of the results were done using R-package [68] as described in the user manual [69]. . . .
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