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Use of genetic data in a species status assessment of the Sicklefin Redhorse (Moxostoma sp.)

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Abstract

Under the United States Endangered Species Act, a species is granted protection if it is in danger of extinction throughout all or a significant portion of its range. Since 2016, the United States Fish and Wildlife Service has adopted a more analytical approach to determining significant portion of its range. Termed Species Status Assessment (SSA), this approach addresses whether loss of individuals from a portion of its range will influence at least one of the conservation biology principles of redundancy (ability to withstand catastrophic events), resiliency (ability to withstand stochastic events), and representation (ability to adapt over time to long-term changes in the environment). Using Sicklefin Redhorse (Moxostoma sp.), we illustrate the use of genetic data to evaluate each SSA metric. We sampled (n = 382) Sicklefin Redhorse from three major river basins throughout its contemporary distribution and estimated genetic parameters using ten microsatellite markers. Using STRUCTURE analyses, we showed that redundancy was three, but our approximate Bayesian computation analysis revealed that this value could be reduced to two if admixture, due to anthropogenic stressors of the 1900s, continues. We used estimates of effective population size (Ne) to measure resiliency and representation and found that all populations showed resiliency and representation with Ne ≥ 479. Genetic monitoring of the Little Tennessee and Tuckasegee populations will be necessary to assess the future status of redundancy for this species. Any reduction in redundancy would warrant further ESA evaluation.

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References

  • Altman HM (2006) Eastern Cherokee fishing. The University of Alabama Press, Tuscaloosa, p 59

    Google Scholar 

  • Anderson EC, Dunham KK (2005) spip 1.0: a program for simulating pedigrees and genetic data in age-structured populations. Mol Ecol Notes 5:459–461

    Article  CAS  Google Scholar 

  • Anderson CN, Ramakrishnan U, Chan YL, Hadly EA (2005) Serial SimCoal: a population genetics model for data from multiple populations and points in time. Bioinformatics 21:1733–1734

    Article  CAS  PubMed  Google Scholar 

  • Beaumont MA, Zhang W, Balding DJ (2002) Approximate Bayesian computation in population genetics. Genetics 162:2025–2035

    PubMed  PubMed Central  Google Scholar 

  • Blum MGB, Francois O (2010) Non-linear regression models for approximate Bayesian computation. Stat Comput 20:63–73

    Article  Google Scholar 

  • Bogan MT, Lytle DA (2011) Severe drought drives novel community trajectories in desert stream pools. Freshw Biol 56:2070–2081

    Article  Google Scholar 

  • Braulik GT, Arshad M, Noureen U, Northridge SP (2014) Habitat fragmentation and species extirpation in freshwater ecosystems; causes of range decline of the Indus River Dolphin (Platanista gangetica minor). PLoS ONE 9:e101657

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Caballero A (1994) Developments in the prediction of effective population size. Heredity 73:657–679

    Article  PubMed  Google Scholar 

  • Chen WJ, Mayden RL (2012) Phylogeny of suckers (Teleostei: Cypriniformes: Catostomidae): further evidence of relationships provided by the single-copy nuclear gene IRBP2. Zootaxa 3586:195–210

    Article  Google Scholar 

  • Csillery K, Blum MGB, Gaggiotti OE, Francois O (2010) Approximate Bayesian computation in practice. Trends Ecol Evol 25:410–418

    Article  PubMed  Google Scholar 

  • Earl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361

    Article  Google Scholar 

  • Earl JE, Nicol S, Wiederholt R, Diffendorfer JE, Semmens D, Flockhart DT, Mattsson BJ, McCracken G, Norris DR, Thogmartin WE, López-Hoffman L (2018) Quantitative tools for implementing the new definition of significant portion of the range in the US Endangered Species Act. Conserv Biol 32:35–49

    Article  PubMed  Google Scholar 

  • Ellegren H, Galtier N (2016) Determinants of genetic diversity. Nat Rev Genet 17:422–433

    Article  CAS  PubMed  Google Scholar 

  • Engen S, Bakke O, Islam A (1998) Demographic and environmental stochasticity-concepts and definitions. Biometrics 1:840–846

    Article  Google Scholar 

  • Etnier DA, Starnes WC (1993) The fishes of Tennessee. University of Tennessee Press, Knoxville

    Google Scholar 

  • Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Ecol 14:2611–2620

    Article  CAS  PubMed  Google Scholar 

  • Excoffier L, Novembre J, Schneider S (2000) Computer note SIMCOAL: a general coalescent program for the simulation of molecular data in interconnected populations with arbitrary demography. J Hered 91:506–509

    Article  CAS  PubMed  Google Scholar 

  • Fagan WF, Holmes EE (2006) Quantifying the extinction vortex. Ecol Lett 9:51–60

    PubMed  Google Scholar 

  • Favrot SD (2009) Sicklefin Redhorse reproductive and habitat ecology in the upper Hiwassee River basin of the southern Appalachian Mountains. Dissertation, North Carolina State University

  • Favrot SD, Kwak TJ (2018) Behavior and reproductive ecology of the Sicklefin Redhorse: an imperiled southern Appalachian Mountain fish. Trans Am Fish Soc 147:2014–2222

    Google Scholar 

  • Frankel OH, Soule ME (1981) Conservation and evolution. Cambridge University Press, Cambridge

    Google Scholar 

  • Frankham R (2005) Conservation biology: ecosystem recovery enhanced by genotypic diversity. Heredity 95:183

    Article  CAS  PubMed  Google Scholar 

  • Frankham R (2010) Challenges and opportunities of genetic approaches to biological conservation. Biol Conserv 143:1919–1927

    Article  Google Scholar 

  • Freake M, O’Neill E, Unger S, Spear S, Routman E (2018) Conservation genetics of eastern hellbenders Cryptobranchus alleganiensis alleganiensis in the Tennessee Valley. Conserv Genet 19:571–585

    Article  CAS  Google Scholar 

  • Harrisson KA, Pavlova A, Telonis-Scott M, Sunnucks P (2014) Using genomics to characterize evolutionary potential for conservation of wild populations. Evol Appl 7:1008–1025

    Article  PubMed  PubMed Central  Google Scholar 

  • Hendry AP, Kinnison MT, Heino M, Day T, Smith TB, Fitt G, Bergstrom CT, Oakeshott J, Jørgensen PS, Zalucki MP, Gilchrist G (2011) Evolutionary principles and their practical application. Evol Appl 4:159–183

    Article  PubMed  PubMed Central  Google Scholar 

  • Hildebrand SF (1932) On a collection of fishes from the Tuckaseegee and upper Catawba River basins NC with a description of a new darter. J Elisha Mitchell Sci Soc 48:50–82

    Google Scholar 

  • Hoban S (2014) An overview of the utility of population simulation software in molecular ecology. Mol Ecol 23:2383–2401

    Article  PubMed  Google Scholar 

  • Hoban S, Bertorelle G, Gaggiotti OE (2012) Computer simulations: tools for population and evolutionary genetics. Nat Rev Genet 13:110–122

    Article  CAS  PubMed  Google Scholar 

  • Hubisz MJ, Falush D, Stephens M, Pritchard JK (2009) Inferring weak population structure with the assistance of sample group information. Mol Ecol Resour 9:1322–1332

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 23:1801–1806

    Article  CAS  PubMed  Google Scholar 

  • Jarne P, Lagoda PJL (1996) Microsatellites from molecules to populations and back. Trends Ecol Evol 11:424–429

    Article  CAS  PubMed  Google Scholar 

  • Jenkins, RE (1999) Sicklefin Redhorse (Moxostoma sp.), undescribed species of sucker (Pisces, Catostomidae) in the upper Tennessee River drainage, North Carolina and Georgia description, aspects of biology, habitat, distribution, and population status. Unpublished report to the U.S. Fish and Wildlife Service, Asheville Field Office, Asheville, NC and the North Carolina Wildlife Resources Commission, Raleigh, NC

  • Kalinowski ST (2005) HP-RARE 10: a computer program for performing rarefaction on measures of allelic richness. Mol Ecol Notes 5:187–189

    Article  CAS  Google Scholar 

  • Kass RE, Raftery AE (1995) Bayes factors. J Am Stat Assoc 90:773–795

    Article  Google Scholar 

  • Keck BP, Near TJ (2010) Geographic and temporal aspects of mitochondrial replacement in Nothonotus darters (Teleostei: Percidae: Etheostomatinae). Evolution 64:1410–1428

    CAS  PubMed  Google Scholar 

  • Kirk H, Freeland JR (2011) Applications and implications of neutral versus non-neutral markers in molecular ecology. Int J Mol Sci 12:3966–3988

    Article  PubMed  PubMed Central  Google Scholar 

  • Lippe C, Dumont P, Bernatchez L (2004) Isolation and identification of 21 microsatellite loci in the Copper Redhorse (Moxostoma hubbsi; Catostomidae) and their variability in other catostomids. Mol Ecol Notes 4:638–641

    Article  CAS  Google Scholar 

  • Markert JA, Champlin DM, Gutjahr-Gobell R, Grear JS, Kuhn A, McGreevy TJ, Roth A, Bagley MJ, Nacci DE (2010) Population genetic diversity and fitness in multiple environments. BMC Evol Biol 10:205

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • McCarthy MA, Keith D, Teitjen J, Burgman MA, Maunder M, Master L, Brook BW, Mace G, Possingham HP, Medellin R, Andelman S, Regan H, Regan T, Ruckelshaus M (2004) Comparing predictions of extinction risk using models and subjective judgement. Acta Oecol 26:67–74

    Article  Google Scholar 

  • Moyer GR, Rousey JD, Cantrell MA (2009) Genetic evaluation of a conservation hatchery program for reintroduction of Sicklefin Redhorse Moxostoma sp. in the Tuckasegee River North Carolina. N Am J Fish Manage 29:1438–1443

    Article  Google Scholar 

  • Newman D, Pilson D (1997) Increased probability of extinction due to decreased genetic effective population size: experimental populations of Clarkia pulchella. Evolution 51:354–362

    Article  PubMed  Google Scholar 

  • Nunney L, Elam DR (1994) Estimating the effective population size of conserved populations. Conserv Biol 8:175–184

    Article  Google Scholar 

  • Ohta T, Kimura M (1973) A model of mutation appropriate to estimate the number of electrophoretically detectable alleles in a finite population. Genet Res 22:201–204

    Article  CAS  PubMed  Google Scholar 

  • Olson-Manning CF, Wagner MR, Mitchell-Olds T (2012) Adaptive evolution: evaluating empirical support for theoretical predictions. Nat Rev Genet 13:867–877

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Palstra FP, Ruzzante DE (2008) Genetic estimates of contemporary effective population size: what can they tell us about the importance of genetic stochasticity for wild population persistence? Mol Ecol 17:3428–3447

    Article  PubMed  Google Scholar 

  • Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes 6:288–295

    Article  Google Scholar 

  • Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinform 28:2537–2539

    Article  CAS  Google Scholar 

  • Piller KR, Bart HL Jr, Hurley DL (2008) Phylogeography of the greenside darter complex, Etheostoma blennioides (Teleostomi: Percidae): a wide-ranging polytypic taxon. Mol Phylogenet Evol 46:974–985

    Article  CAS  PubMed  Google Scholar 

  • Pujol B, Pannell JR (2008) Reduced responses to selection after species range expansion. Science 321:96

    Article  CAS  PubMed  Google Scholar 

  • Quinn JF, Hastings A (1987) Extinction in subdivided habitats. Conserv Biol 1:198–209

    Article  Google Scholar 

  • R Development Core Team (2014) R: a language and environment for statistical computing R Foundation for Statistical Computing Vienna v. 3.1.1

  • Raymond M, Rousset F (1995) GENEPOP (version 12): population genetics software for exact tests and ecumenicism. J Hered 86:248–249

    Article  Google Scholar 

  • Reed DH, Frankham R (2003) Correlation between fitness and genetic diversity. Conserv Biol 17:230–237

    Article  Google Scholar 

  • Robinson JD, Moyer GR (2012) Linkage disequilibrium and effective population size when generations overlap. Evol Appl 6:290–302

    Article  PubMed  PubMed Central  Google Scholar 

  • Robinson JD, Simmons JW, Williams AS, Moyer GR (2013) Population structure and genetic diversity in the endangered bluemask darter (Etheostoma akatulo). Conserv Genet 14:79–92

    Article  Google Scholar 

  • Rosenberg NA (2004) DISTRUCT: a program for the graphical display of population structure. Mol Ecol Notes 4:137–138

    Article  Google Scholar 

  • Schwartz MK, Luikart G, Waples RS (2007) Genetic monitoring as a promising tool for conservation and management. Trends Ecol Evol 22:25–33

    Article  PubMed  Google Scholar 

  • Shaffer M, Stein BA (2000) Safeguarding our precious heritage. In: Stein BA, Kutner LS, Adams JS (eds) Precious heritage: the status of biodiversity in the United States. Oxford University Press, New York, pp 301–322

    Google Scholar 

  • Slatkin M (1985) Gene flow in natural populations. Annu Rev Ecol Evol Syst 1:393–430

    Article  Google Scholar 

  • Smith DR, Allan NL, McGowan CP, Szymanski JA, Oetker SR, Bell HM (2018) Development of a species status assessment process for decisions under the US Endangered Species Act. J Fish Wildl Manage 9:302–320

    Article  Google Scholar 

  • Storfer A (1999) Gene flow and endangered species translocations: a topic revisited. Biol Conserv 87:173–180

    Article  Google Scholar 

  • USFWS (2014) Final policy on interpretation of the phrase “significant portion of its range” in the Endangered Species Act’s definitions of “endangered species” and “threatened species”. Federal Regist 79:37578–37612

    Google Scholar 

  • USFWS (2016) 12-Month findings on petitions to list 10 species as endangered or threatened species. Federal Regist 81(69425):69442

    Google Scholar 

  • Waples RS, Do C (2008) LDNe: a program for estimating effective population size from data on linkage disequilibrium. Mol Ecol Resour 8:753–756

    Article  PubMed  Google Scholar 

  • Waples RS, Do C (2010) Linkage disequilibrium estimates of contemporary N e using highly variable genetic markers: a largely untapped resource for applied conservation and evolution. Evol Appl 3:244–262

    Article  PubMed  Google Scholar 

  • Waples RS, England PR (2011) Estimating contemporary effective population size on the basis of linkage disequilibrium in the face of migration. Genetics 189:633–644

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 15:1419–1439

    Article  CAS  PubMed  Google Scholar 

  • Waples RS, Adams PB, Bohnsack J, Taylor BL (2007) A biological framework for evaluating whether a species is threatened or endangered in a significant portion of its range. Conserv Biol 21:964–974

    Article  PubMed  Google Scholar 

  • Waples RS, Antao T, Luikart G (2014) Effects of overlapping generations on linkage disequilibrium estimates of effective population size. Genetics 197:769–780

    Article  PubMed  PubMed Central  Google Scholar 

  • Williams MR (1987) The history of Jackson County. The Jackson County Historical Association, Sylva

    Google Scholar 

  • Wolf S, Hartl B, Carroll C, Neel MC, Greenwald DN (2015) Beyond PVA: why recovery under the Endangered Species Act is more than population viability. Bioscience 65:200–207

    Article  Google Scholar 

  • Young JAM, Koops MA (2014) Population modelling of Black Redhorse (Moxostoma duquesni) in Canada. Can Sci Advis Sec Res Doc 2014/020 iv + 14p

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Acknowledgements

Field assistance was provided by Robert Jenkins, John Fridell, Steve Fraley, T.R. Russ, Tiffany Penland, David Yow, Powell Wheeler, Amanda Bushon, Angie Rodgers, Dustin Rodgers, David Huffstettler, Crystal Ruble, Missy Petty, Rebecca Xiques, Pat Rakes, Dallas Bradley, Mike Lavoie, Blue Welch, Scott Favrot, Hannah Shively, Calvin Yonce, Jason Mays, Jimmy Jenkins, Tomas Ivaskukas, Bob Butler, Dan Everson, Byron Hamstead, Jan Gay, Jenny Sanders, and Kyle Stowe. Robert E. Jenkins deserves the utmost credit for recognizing this distinct fish, and tirelessly imparting an understanding of its distribution and life history to the next generation of scientists. Funding for this project was provided by the U.S. Fish and Wildlife Service. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the U.S. Fish and Wildlife Service.

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Correspondence to Gregory R. Moyer.

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Moyer, G.R., Bohn, S., Cantrell, M. et al. Use of genetic data in a species status assessment of the Sicklefin Redhorse (Moxostoma sp.). Conserv Genet 20, 1175–1185 (2019). https://doi.org/10.1007/s10592-019-01202-3

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