Conservation Genetics

, Volume 16, Issue 6, pp 1345–1357 | Cite as

Population genetics of the speckled peacock bass (Cichla temensis), South America’s most important inland sport fishery

  • Stuart C. WillisEmail author
  • Kirk O. Winemiller
  • Carmen G. Montaña
  • Jason Macrander
  • Paul Reiss
  • Izeni P. Farias
  • Guillermo Ortí
Research Article


The Neotropics harbor the world’s most diverse freshwater fish fauna, with many of these species supporting major commercial, subsistence, or sport fisheries. Knowledge of population genetic structure is available for very few Neotropical fishes, thereby restricting management. To address this need, we examined population genetic variation in mtDNA control region sequences and twelve microsatellite loci in the speckled or barred peacock bass, Cichla temensis. Moderate and statistically significant genetic divergence among localities indicates that migration is low in this species, implying that populations inhabiting tributaries or even smaller spatial units should constitute management units. Analysis of molecular variance of mtDNA sequences identified six areas with largely exclusive haplotype clades, and a seventh area of high admixture, but major drainage basins harbored non-monophyletic haplotype groups. On the other hand, molecular variation in the microsatellite data was best explained by drainage basin and, subsequently, by the seven areas. Populations in these seven areas could be considered evolutionarily significant units (ESUs), and, therefore, we tested hypotheses explaining the discordant signal of mtDNA and microsatellite data using approximate Bayesian computation. This analysis indicated that the divergence of mtDNA clades preceded the divergence of contemporary ESUs across basins, with subsequent lineage sorting among ESUs due to reduced gene flow. Available genetic and ecological information indicates that C. temensis populations of major tributary rivers should be managed as separate stocks that likely are adapted to local environmental conditions.


Amazon Fish stock Evolutionary significant units Lineage sorting Management unit 



The authors appreciate all those who contributed tissues or assisted in collection for this project, in particular V. Machado, N. Meliciano, and D. Ribeiro. Tissues were collected, stored, and utilized under permits from the Ministerio de Ambiente y Recursos Naturales (MARN) in Venezuela and Instituto Chico Mendes de Conservação da Biodiversidade (IBAMA/ICMBio) in Brazil (permit for collection No. 031/2003, 045/IBAMA, 148/2006- DIFAP/IBAMA, permit for access to genetic resources in Brazil No. 034/2005/IBAMA, and Permanent IBAMA License 11325– 1/2007). We acknowledge funding from the US National Science Foundation, (OISE DDEP to G.Orti and S. Willis), George Washington University, UNL Research Cluster, International Foundation for Science, CNPq (CNPq/PPG7 (557090/2005–2009), (554057/2006–2009)), and FAPEAM.

Supplementary material

10592_2015_744_MOESM1_ESM.tif (340 kb)
Fig. S1 Historical scenarios tested with DIY-ABC. Parameter prior ranges are shown, and below are constraints on divergence times. Terminal branch colors correspond to ESUs in Fig. 1 (TIFF 341 kb)
10592_2015_744_MOESM2_ESM.tif (309 kb)
Fig. S2 Haplotype network of mtCR sequences. Color corresponds to regional evolutionary significant units. Each line represents a single mutation, except where indicated by cross-hatched lines. A missing haplotype was indicated by an unfilled circle (TIFF 309 kb)
10592_2015_744_MOESM3_ESM.pdf (58 kb)
Fig. S3 Principal components analysis of genotypes of twelve microsatellite loci. Colors and symbols correspond to inferred ESUs and localities within ESUs respectively (PDF 58 kb)
10592_2015_744_MOESM4_ESM.tif (384 kb)
Fig. S4 Principal components analysis of simulated and observed vectors for DIYABC scenarios 1 and 2. A) Vectors simulated from the prior distribution of both scenarios. B) Vectors simulated from the prior (unfilled) and posterior (filled) of scenario 1. C) Vectors simulated from the prior (unfilled) and posterior (filled) of scenario 2 (TIFF 385 kb)
10592_2015_744_MOESM5_ESM.tif (401 kb)
Fig. S5 Principal components analysis of simulated and observed vectors for DIYABC scenarios 2 and 3. A) Vectors simulated from the prior distribution of both scenarios. B) Vectors simulated from the prior (unfilled) and posterior (filled) of scenario 2. C) Vectors simulated from the prior (unfilled) and posterior (filled) of scenario 3 (TIFF 401 kb)
10592_2015_744_MOESM6_ESM.tif (126 kb)
Fig. S6 Posterior probability over different subsets of simulated vectors closest to the observed vector. Top panels: scenarios 1 and 2. Lower panels: scenarios 2 and 3 (TIFF 127 kb)
10592_2015_744_MOESM7_ESM.xlsx (61 kb)
Table S1 Values of RST (Slatkin, 1995) below diagonal and FST′ (Miermans, 2006) above diagonal among localities. Locality codes follow Table 1 (XLSX 62 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Stuart C. Willis
    • 1
    • 2
    Email author
  • Kirk O. Winemiller
    • 3
  • Carmen G. Montaña
    • 4
  • Jason Macrander
    • 5
  • Paul Reiss
    • 6
  • Izeni P. Farias
    • 7
  • Guillermo Ortí
    • 8
  1. 1.School of Biological SciencesUniversity of Nebraska-LincolnLincolnUSA
  2. 2.Harte Research Institute for Gulf of Mexico StudiesTexas A&M University-Corpus ChristiCorpus ChristiUSA
  3. 3.Department Wildlife and Fisheries SciencesTexas A&M UniversityCollege StationUSA
  4. 4.Department of Applied EcologyNorth Carolina State UniversityRaleighUSA
  5. 5.Department of Evolution, Ecology, and Organismal BiologyThe Ohio State UniversityColumbusUSA
  6. 6.Department of Ecology, Evolution, and Natural ResourcesRutgers UniversityNew BrunswickUSA
  7. 7.Laboratório de Evolução e Genética Animal, ICBUniversidade Federal do AmazonasManausBrazil
  8. 8.Department of Biological SciencesThe George Washington UniversityWashingtonUSA

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