Advertisement

Biodiversity and Conservation

, Volume 26, Issue 6, pp 1275–1293 | Cite as

Conservation implications of significant population differentiation in an endangered estuarine seahorse

  • T. K. Mkare
  • B. Jansen van Vuuren
  • P. R. Teske
Original Paper
Part of the following topical collections:
  1. Coastal and marine biodiversity

Abstract

The spatial distribution of a species’ genetic diversity can provide insights into underlying evolutionary, ecological and environmental processes, and can contribute information towards the delineation of conservation units. The Knysna seahorse, Hippocampus capensis, is endangered and occurs in only three estuaries on the warm-temperate south coast of South Africa: Knsyna, Keurbooms and Swartvlei. Population sizes in the latter two estuaries have been very small for a prolonged period of time, and the populations residing in them may thus benefit from translocations as a means of increasing population sizes and possibly also genetic diversity. However, information on whether these three estuaries constitute distinct conservation units that warrant separate management is presently lacking. Here, we used genetic information from mitochondrial (control region) and nuclear microsatellite loci to assess the genetic diversity and spatial structure across the three estuaries, and also whether translocations should be included in the management plan for the Knysna seahorse. Although each population had a unique combination of alleles, and clustering methods identified the Swartvlei Estuary as being distinct from the others, levels of genetic admixture were high, and there was no evidence for reciprocal monophyly that would indicate that each estuary has a unique demographic history. On these grounds, we suggest recognising the three populations as a single evolutionarily significant unit (ESU), and encourage translocations between them to ensure the species’ long-term survival.

Keywords

Evolutionarily significant units (ESU) Endangered estuarine fish Population differentiation Seahorse Hippocampus capensis South Africa 

Notes

Acknowledgements

We are grateful to Louw Claassens (Knysna Basin Project), Zeen Weight, Fatima Daniels and Sophie Bader for assisting in the acquisition of samples from the Knysna and Keurbooms estuaries, and to an anonymous benefactor for providing free accommodation for the duration of the fieldwork. Sampling permits were granted by SANParks and CapeNature. This study received funding from the Rufford Foundation (small grant 14490-1 awarded to PR Teske), and from the University of Johannesburg (URC Grant). T. K. M. acknowledges the University of Johannesburg for awarding him a Global Excellence and Stature (GES) scholarship.

Compliance with ethical standards

Conflict of interest

The authors have declared no conflict of interest.

Ethical approval

Ethical approval to collect genetic samples was granted by the Ethics Committee of the University of Johannesburg, South Africa. All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.

Supplementary material

10531_2017_1300_MOESM1_ESM.doc (708 kb)
Supplementary material 1 (DOC 709 kb)

References

  1. Allanson B, Herbert DG (2005) A newly discovered population of the critically endangered false limpet Siphonaria compressa Allanson, 1958 (Pulmonata: Siphonariidae), with observations on its reproductive biology. S Afr J Sci 101:95–97Google Scholar
  2. Allendorf FW (1986) Genetic drift and the loss of alleles versus heterozygosity. Zoo Biol 5:181–190CrossRefGoogle Scholar
  3. Allendorf FW, Luikart G (2007) Conservation and the genetics of populations. Blackwell Publishing, MaldenGoogle Scholar
  4. Anderson EC, Barry PD (2015) Interpreting the FLOCK algorithm from a statistical perspective. Mol Ecol Resour 15:1020–1030PubMedCrossRefGoogle Scholar
  5. Arif IA, Khan HA (2009) Molecular markers for biodiversity analysis of wildlife animals: a brief review. Anim Biodivers Conserv 32:9–17Google Scholar
  6. Avise JC (2000) Phylogeography: The history and formation of species. Harvard University Press, CambridgeGoogle Scholar
  7. Bandelt H, Forster P, Röhl A (1999) Median-joining networks for inferring intraspecific phylogenies. Mol Biol Evol 16:37–48PubMedCrossRefGoogle Scholar
  8. Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F (2001) Genetix 4. 02, logiciel sous Windows™ pour lagntique des populations. Laboratoire génome, populations, interactions: CNRS UMR 5000, Université de Montpellier II, MontpellierGoogle Scholar
  9. Bell EM, Lockyear JF, McPherson JM, Marsden AD, Vincent ACJ (2003) First field studies of an Endangered South African seahorse, Hippocampus capensis. Environ Biol Fishes 67:35–46CrossRefGoogle Scholar
  10. Benson JF, Hostetler JA, Onorato DP, Johnson WE, Roelke ME, O’Brien SJ, Jansen D, Oli MK (2011) Intentional genetic introgression influences survival of adults and subadults in a small, inbred felid population. J Anim Ecol 80:958–967PubMedCrossRefGoogle Scholar
  11. Bouzat JL, Johnson JA, Toepfer JE, Simpson SA, Esker TL, Westemeier RL (2009) Beyond the beneficial effects of trans-locations as an effective tool for the genetic restoration of isolated populations. Conserv Genet 10:191–201CrossRefGoogle Scholar
  12. Brownstein MJ, Carpten JD, Smith JR (1996) Modulation of non-templated nucleotide addition by Taq DNA polymerase: primer modifications that facilitate genotyping. Biotechniques 20:1004–1010PubMedGoogle Scholar
  13. Bruvo RA, Michiels NK, D’Souza TG, Schulenburg H (2004) A simple method for the calculation of microsatellite genotype distances irrespective of ploidy level. Mol Ecol 13:2101–2106PubMedCrossRefGoogle Scholar
  14. Cahill AE, Levinton JS (2016) Genetic differentiation and reduced genetic diversity at the northern range edge of two species with different dispersal modes. Mol Ecol 25:515–526PubMedCrossRefGoogle Scholar
  15. Caldwell IR, Vincent ACJ (2012) Revisiting two sympatric European seahorse species: apparent decline in the absence of exploitation. Aquat Conserv Mar Freshw Ecosyst 22:427–435CrossRefGoogle Scholar
  16. Carlson SM, Cunningham CJ, Westley PAH (2015) Evolutionary rescue in a changing world. Trends Ecol Evol 29:521–530CrossRefGoogle Scholar
  17. Crandall KA, Bininda-Emonds ORP, Mace GM, Wayne RK (2000) Considering evolutionary processes in conservation biology. Trends Ecol Evol 15:290–295PubMedCrossRefGoogle Scholar
  18. Czembor CA, Bell EM (2012) Hippocampus capensis. The IUCN Red List of Threatened Species 2012:e.T10056A495994Google Scholar
  19. Davies DH (1948) A new goby from the Knysna River. Ann Mag Nat Hist 1:357–376CrossRefGoogle Scholar
  20. Dixo M, Metzger JP, Morgante JS, Zamudio KR (2009) Habitat fragmentation reduces genetic diversity and connectivity among toad populations in the Brazilian Atlantic Coastal Forest. Biol Cons 142:1560–1569CrossRefGoogle Scholar
  21. Doyle JJ, Doyle JL (1987) A rapid DNA isolation procedure for small quatities of fresh leaf tissue. Phytochem Bull 19:11–15Google Scholar
  22. Doyle JJ, Doyle JL (1990) Isolation of plant DNA from fresh tissue. Focus 12:13–15Google Scholar
  23. Duchesne P, Turgeon J (2012) FLOCK provides reliable solutions to the “number of populations” problem. J Hered 103:734–743PubMedCrossRefGoogle Scholar
  24. Earl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361CrossRefGoogle Scholar
  25. Edmands S, Timmerman CC (2003) Modelling factors affecting the severity of outbreeding depression. Conserv Biol 17:883–892CrossRefGoogle Scholar
  26. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620PubMedCrossRefGoogle Scholar
  27. Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 10:564–567PubMedCrossRefGoogle Scholar
  28. Fahrig L (2003) Effects of habitat fragmentation on biodiversity. Ann Rev Ecol Evol Syst 34:487–515Google Scholar
  29. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure: extensions to linked loci and correlated allele frequencies. Genetics 164:1567–1587PubMedPubMedCentralGoogle Scholar
  30. Frankham R (2015) Genetic rescue of small inbred populations: meta-analysis reveals large and consistent benefits of gene flow. Mol Ecol 24:2610–2618PubMedCrossRefGoogle Scholar
  31. Frankham R, Ballou JD, Eldridge MDB, Lacy RC, Ralls K, Dudash MR, Fenster CB (2011) Predicting the probability of outbreeding depression. Conserv Biol 25:465–475PubMedCrossRefGoogle Scholar
  32. Fraser DJ, Bernatchez L (2001) Adaptive evolutionary conservation: towards a unified concept for defining conservation units. Mol Ecol 10:2741–2752PubMedCrossRefGoogle Scholar
  33. Frommen JG, Bakker TCM (2006) Inbreeding avoidance through non-random mating in sticklebacks. Biol Lett 2:232–235PubMedPubMedCentralCrossRefGoogle Scholar
  34. Galbusera PHA, Gillemot S, Jour P, Teske PR, Hellemans B, Volckaert FAMJ (2007) Isolation of microsatellite markers for the endangered Knysna seahorse Hippocampus capensis and their use in the detection of a genetic bottleneck. Mol Ecol Notes 7:638–640CrossRefGoogle Scholar
  35. Hale ML, Burg TM, Steeves TE (2012) Sampling for microsatellite-based population genetic studies: 25 to 30 individuals per population is enough to accurately estimate allele frequencies. PLoS ONE 7(9):e45170PubMedPubMedCentralCrossRefGoogle Scholar
  36. Hanski I (1999) Metapopulation ecology. Oxford University Press, OxfordGoogle Scholar
  37. Heber S, Varsani A, Kuhn S, Girg A, Kempenaers B, Briskie J (2013) The genetic rescue of two bottlenecked South Island robin populations using translocations of inbred donors. Proc R Soc B 280:2012–2228Google Scholar
  38. Hedrick PW, Hurt CR (2012) Conservation genetics and evolution in an endangered species: research in Sonoran topminnows. Evol Appl 5:806–819PubMedPubMedCentralCrossRefGoogle Scholar
  39. Heller R, Siegismund HR (2009) Relationship between three measures of genetic differentiation G ST, D EST and GST: how wrong have we been? Mol Ecol 18:2080–2083PubMedCrossRefGoogle Scholar
  40. Henle K, Lindenmayer DB, Margules CR, Saunders DA, Wissel C (2004) Species survival in fragmented landscapes: where are we now? Biodivers Conserv 13:1–8CrossRefGoogle Scholar
  41. Hoareau TB, Klopper AW, Dos Santos SMR, Oosthuizen CJ, Bloomer P (2015) Evaluating the resolution power of new microsatellites for species identi cation and stock delimitation in the Cape hakes Merluccius paradoxus and Merluccius capensis (Teleostei: Merlucciidae). J Fish Biol 86:1650–1657PubMedCrossRefGoogle Scholar
  42. Hogg JT, Forbes SH, Steele BM, Luikart G (2006) Genetic rescue of an insular population of large mammals. Proc R Soc B 273:1491–1499PubMedPubMedCentralCrossRefGoogle Scholar
  43. Hubisz MJ, Falush D, Stephens M, Pritchard JK (2009) Inferring weak population structure with the assistance of sample group information. Mol Ecol Resour 9:1322–1332PubMedPubMedCentralCrossRefGoogle Scholar
  44. Jakobsson M, Rosenberg NA (2007) CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23:1801–1806PubMedCrossRefGoogle Scholar
  45. Johansson M, Primmer CR, Merila J (2007) Does habitat fragmentation reduce fitness and adaptability? A case study of the common frog (Rana temporaria). Mol Ecol 16:2693–2700PubMedCrossRefGoogle Scholar
  46. Jombart T (2008) adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405PubMedCrossRefGoogle Scholar
  47. Jombart T, Collins C (2015) A tutorial for discriminant analysis of principal components (DAPC) using adegenet 2.0.0. http://adegenet.r-forge.r-project.org/files/tutorial-dapc.pdf Accessed 23 Jun 2015
  48. Jombart T, Devillard S, Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet 11:94PubMedPubMedCentralCrossRefGoogle Scholar
  49. Jones AG, Kvarnemo C, Moore GI, Simmons LW, Avise JC (1998) Microsatellite evidence for monogamy and sex-biased recombination in the Western Australian seahorse Hippocampus angustus. Mol Ecol 7:1497–1505PubMedCrossRefGoogle Scholar
  50. Jost L (2008) G ST and its relatives do not measure differentiation. Mol Ecol 17:4015–4026PubMedCrossRefGoogle Scholar
  51. Kalinowski ST (2004) Counting alleles with rarefaction: Private alleles and hierarchical sampling designs. Conserv Genet 5:539–543CrossRefGoogle Scholar
  52. Kalinowski ST (2005) HP-RARE 1.0: a computer program for performing rarefaction on measures of allelic richness. Mol Ecol Notes 5:187–189CrossRefGoogle Scholar
  53. Kamvar ZN, Tabima JF, Grunwald NJ (2014) Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2:e281PubMedPubMedCentralCrossRefGoogle Scholar
  54. Kimura M, Crow JF (1964) The number of alleles that can be maintained in a finite population. Genetics 49:725–738PubMedPubMedCentralGoogle Scholar
  55. Kimura M, Ohta T (1978) Stepwise mutation model and distribution of allelic frequencies in finite population. Proc Natl Acad Sci USA 75:2868–2872PubMedPubMedCentralCrossRefGoogle Scholar
  56. Kronenberger JA, Funk WC, Smith JW, Fitzpatrick SW, Angeloni LM, Broder ED, Ruell EW (2016) Testing the demographic effects of divergent immigrants on small populations of Trinidadian guppies. Anim Conserv. doi: 10.1111/acv.12286 Google Scholar
  57. Kumar S, Stecher G, Tamura K (2016) MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol 33:1870–1874PubMedCrossRefGoogle Scholar
  58. Largier JL, Attwood C, Harcourt-Baldwin JL (2000) The hydrographic character of the Knysna Estuary. Trans Roy Soc S Afr 55:107–122CrossRefGoogle Scholar
  59. Larson S, Ramsey C, Tinnemore D, Amemiya C (2014) Novel microsatellite loci variation and population genetics within Leafy Seadragons, Phycodurus eques. Diversity 6:33–42CrossRefGoogle Scholar
  60. Leigh JW, Bryant D (2015) popart: full-feature software for haplotype network construction. Methods Ecol Evol 6:1110–1116CrossRefGoogle Scholar
  61. Lockyear JF, Hecht T, Kaiser H, Teske PR (2006) The distribution and abundance of the endangered Knysna seahorse Hippocampus capensis (Pisces: Syngnathidae) in South African estuaries. Afr J Aquat Sci 31:275–283CrossRefGoogle Scholar
  62. López A, Vera M, Planas M, Bouza C (2015) Conservation genetics of threatened Hippocampus guttulatus in vulnerable habitats in NW Spain: temporal and spatial stability of wild populations with flexible polygamous mating system in captivity. PLoS ONE 10:e0117538PubMedPubMedCentralCrossRefGoogle Scholar
  63. Ludwig A (2006) A sturgeon view on conservation genetics. Eur J Wildl Res 52:3–8CrossRefGoogle Scholar
  64. Martin-Smith KM, Vincent ACJ (2005) Seahorse declines in the Derwent estuary, Tasmania in the absence of fishing pressure. Biol Cons 123:533–545CrossRefGoogle Scholar
  65. McLean J, Taylor E (2001) Resolution of population structure in a species with high gene flow: microsatellite variation in the eulachon (Osmeridae: Thaleichthys pacificus). Mar Biol 139:411–420CrossRefGoogle Scholar
  66. Meirmans PG, Hedrick PW (2011) Assessing population structure: F ST and related measures. Mol Ecol Resour 11:5–18PubMedCrossRefGoogle Scholar
  67. Miller JM, Poissant J, Hogg JT, Coltman DW (2012) Genomic consequences of genetic rescue in an insular population of bighorn sheep (Ovis canadensis). Mol Ecol 21:1583–1596PubMedCrossRefGoogle Scholar
  68. Montoya-Maya PH, Schleyer MH, Macdonald AHH (2016) Limited ecologically relevant genetic connectivity in the south-east African coral populations calls for reef-level management. Mar Biol 163:171CrossRefGoogle Scholar
  69. Moritz C (1994) Defining ‘evolutionarily significant units’ for conservation. Trends Ecol Evol 9:373–375PubMedCrossRefGoogle Scholar
  70. Nei M (1987) Molecular evolutionary genetics. Columbia University Press, New YorkGoogle Scholar
  71. Nickel J, Cursons R (2012) Genetic diversity and population structure of the pot-belly seahorse Hippocampus abdominalis in New Zealand. New Zeal J Mar Fresh 46:207–218CrossRefGoogle Scholar
  72. Paetkau D, Calvert W, Sterling I, Strobeck C (1995) Microsatellite analysis of population-structure in Canadian polar bears. Mol Ecol 4:347–354PubMedCrossRefGoogle Scholar
  73. Palsbøll PJ, Bérubé M, Allendorf FW (2007) Identification of management units using population genetic data. Trends Ecol Evol 22:11–16PubMedCrossRefGoogle Scholar
  74. Panithanarak T, Karuwancharoen R, Na-Nakorn U, Nguyen TTT (2010) Population genetics of the spotted seahorse (Hippocampus kuda) in Thai waters: implications for conservation. Zool Stud 49:564–576Google Scholar
  75. Park SDE (2001) The Excel Microsatellite Toolkit (v3.1). Animal Genomics Laboratory, UCD, IrelandGoogle Scholar
  76. Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28:2537–2539PubMedPubMedCentralCrossRefGoogle Scholar
  77. Petit RJ, Mousadik AE, Pons O (1998) Identifying populations for conservation on the basis of genetic markers. Conserv Biol 12:844–855CrossRefGoogle Scholar
  78. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedPubMedCentralGoogle Scholar
  79. R Development Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org/
  80. Raymond M, Rousset F (1995) genepop, version 1.2: population genetics software for exact tests and ecumenicism. J Hered 86:248–249CrossRefGoogle Scholar
  81. Riley AK (1986) Aspekte van die soutgehalte toleransie van die Knysna seeperdjie, Hippocampus capensis (Boulenger, 1900) in die Knysna estuarium. Unpublished report. Grahamstown, Rhodes UniversityGoogle Scholar
  82. Rose E, Small CM, Saucedo HA, Harper C, Jones AG (2014) Genetic evidence for monogamy in the dwarf seahorse, Hippocampus zosterae. J Hered 105:828–833PubMedCrossRefGoogle Scholar
  83. Rosenberg NA (2004) Distruct: a program for the graphical display of population structure. Mol Ecol Notes 4:137–138CrossRefGoogle Scholar
  84. Rousset F (2008) Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux. Mol Ecol Resour 8:103–106PubMedCrossRefGoogle Scholar
  85. Russell IA (1994) Mass mortality of marine and estuarine fish in the Swarlvlei and Wilderness lake systems, southern cape. Sth Afr J Aquat Sci 20:93–96Google Scholar
  86. Russell IA (1996) Water quality in the Knysna estuary. Koedoe 39:1–8CrossRefGoogle Scholar
  87. Russell IA (2015) Spatio-temporal variability of five surface water quality parameters in the Swartvlei estuarine lake system, South Africa. Afr J Aquat Sci 40:119–131CrossRefGoogle Scholar
  88. Ryder OA (1986) Species conservation and systematics: the dilemma of subspecies. Trends Ecol Evol 1:9–10CrossRefGoogle Scholar
  89. Ryman N, Palm S (2006) POWSIM: a computer program for assessing statistical power when testing for genetic differentiation. Mol Ecol Notes 6:600–602CrossRefGoogle Scholar
  90. Schuelke M (2000) An economic method for the fluorescent labelling of PCR fragments. Nat Biotechnol 18:233–234PubMedCrossRefGoogle Scholar
  91. Schwartz MK, Luikart G, Waples RS (2007) Genetic monitoring as a promising tool for conservation and management. Trends Ecol Evol 22:25–33PubMedCrossRefGoogle Scholar
  92. Selkoe KA, Toonen RJ (2006) Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecol Lett 9:615–629PubMedCrossRefGoogle Scholar
  93. Stearns SC, Sage RD (1980) Maladaptation in a marginal population of the Mosquito fish, Gambusia affinis. Evolution 34:65–75CrossRefGoogle Scholar
  94. Szulkin M, Stopher KV, Pemberton JM, Reid JM (2016) Inbreeding avoidance, tolerance, or preference in animals? Trends Ecol Evol 28:205–211CrossRefGoogle Scholar
  95. Tallmon DA, Luikart G, Waples RS (2004) The alluring simplicity and complexity reality of genetic rescue. Trends Ecol Evol 19:489–496PubMedCrossRefGoogle Scholar
  96. Teske PR, Cherry MI, Matthee CA (2003) Population genetics of the endangered Knysna seahorse, Hippocampus capensis. Mol Ecol 12:1703–1715PubMedCrossRefGoogle Scholar
  97. Teske PR, Hamilton H, Palsboll PJ, Choo CK, Gabr H, Lourie SA, Santos M, Sreepada A, Cherry MI, Matthee CA (2005) Molecular evidence for long-distance colonization in an Indo-Pacific seahorse lineage. Mar Ecol Prog Ser 286:249–260CrossRefGoogle Scholar
  98. Teske PR, Lockyear JF, Hecht T, Kaiser H (2007) Does the endangered Knysna seahorse, Hippocampus capensis, have a preference for aquatic vegetation type, cover or height? Afr Zool 42:23–30CrossRefGoogle Scholar
  99. Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTALW: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position–specific gap penalties and weight matrix choice. Nucleic Acids Res 22:4673–4680PubMedPubMedCentralCrossRefGoogle Scholar
  100. Tyson PD (1971) Outeniqualand: The George-Knysna area. The South African landscape, No. 2. South African Geographical Society, BraamfonteinGoogle Scholar
  101. van de Vliet MS, Diekmann OE, Serrão ETA (2009) Highly polymorphic microsatellite markers for the short-snouted seahorse (Hippocampus hippocampus), including markers from a closely related species the long-snouted seahorse (Hippocampus guttulatus). Conserv Genet Resour 1:93–96CrossRefGoogle Scholar
  102. van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) MICROCHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes 4:535–538CrossRefGoogle Scholar
  103. Volkmann L, Martyn I, Moulton V, Spillner A, Mooers AO (2014) Prioritizing populations for conservation using phylogenetic networks. PLoS ONE 9:e88945PubMedPubMedCentralCrossRefGoogle Scholar
  104. Wan QH, Wu H, Fujihara T, Fang SG (2004) Which genetic marker for which conservation genetics issue? Electrophoresis 25:2165–2176PubMedCrossRefGoogle Scholar
  105. Waples RS, Gaggiotti O (2006) What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity. Mol Ecol 15:1419–1439PubMedCrossRefGoogle Scholar
  106. Weeks AR, Sgro CM, Young AG, Frankham R, Mitchell NJ, Miller KA, Byrne M, Coates DJ, Eldridge MDB, Sunnucks P, Breed MF, James EA, Hoffmann AA (2011) Assessing the benefits and risks of translocations in changing environments: a genetic perspective. Evol Appl 4:709–725PubMedPubMedCentralCrossRefGoogle Scholar
  107. Whiteley AR, Fitzpatrick SW, Funk WC, Tallmon DA (2015) Genetic rescue to the rescue. Trends Ecol Evol 30:42–49PubMedCrossRefGoogle Scholar
  108. Whitfield AK (1989) Ichthyoplankton interchange in the mouth region of a southern African etuary. Mar Ecol Prog Ser 54:25–33CrossRefGoogle Scholar
  109. Whitfield AK, Baliwe NG (2013) A century of science in South African estuaries: Bibliography and review of research trends. SANCOR Occasional Report No. 7: 289 ppGoogle Scholar
  110. Woodall LC, Koldewey HJ, Boehm JT, Shaw PW (2015) Past and present drivers of population structure in a small coastal fish, the European long snouted seahorse Hippocampus guttulatus. Conserv Genet 16:1139–1153CrossRefGoogle Scholar
  111. Wright S (1951) The genetical structure of populations. Ann Eugenic 15:323–354CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • T. K. Mkare
    • 1
    • 2
  • B. Jansen van Vuuren
    • 1
  • P. R. Teske
    • 1
  1. 1.Molecular Zoology Laboratory and Centre for Ecological Genomics and Wildlife Conservation, Department of ZoologyUniversity of JohannesburgJohannesburgSouth Africa
  2. 2.Kenya Marine and Fisheries Research InstituteMombasaKenya

Personalised recommendations