Abstract
Genetic patterns are shaped by the interaction of different factors such as distance, barriers, landscape resistance and local environment. The relative importance of these processes may vary for species with different ecological traits. Here we compared two related Amazonian riverine turtle species (Podocnemis erythrocephala and Podocnemis sextuberculata) with distinct dispersal abilities to assess how differently local and connectivity variables influence their genetic patterns. We used a total of 609 genetic samples to estimate mitochondrial (mtDNA) genetic diversity and differentiation for each locality. We applied model selection on models associating genetic diversity to local variables representing hypotheses of climate and primary productivity, water level variation, hunting pressure and downstream increase in genetic diversity. We modeled the relationship of genetic differentiation with connectivity variables representing hypotheses of isolation by distance (IBD), isolation by resistance (IBR) and isolation by barrier (IBB). Model selection for genetic diversity was only important (excluded the null model) for the high-dispersal species (P. sextuberculata), with best models including hypotheses of productivity and hunting pressure. Genetic diversity was higher in more productive sites and in sites with higher concentration of villages (opposed to expected). Although a variable importance testing showed low importance for connectivity models, IBB (Amazon River) and IBR (resistance by current and past climatic suitability and river color) models explained more genetic differentiation turnover than IBD (riverway distance). Models explained a higher percentage of genetic differentiation for the low-dispersal species (P. erythrocephala), with Amazon River as main predictor. We show that, although local variables are often overlooked in riverscape genetics studies, they can influence intrapopulacional genetic diversity of aquatic species, even those with high dispersal ability. By applying a resistance-model framework and by using riverscape genetics factors relevant in basin-wide context, we provide a novel approach to investigate genetic patterns of other aquatic vertebrates in fluvial systems.
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References
Alcântara AS, Félix-Silva D, Pezzuti JCB (2013) Effects of the hydrological cycle and human settlements on the population status of Podocnemis unifilis (Testudines: Podocnemididae) in the Xingu River, Brazil. Chelonian Conserv Biol 12:134–142
Alho CJR, Pádua LFM (1982) Sincronia entre o regime de vazante do rio e o comportamento de nidificaçào da tartaruga da Amazônia Podocnemis expansa (Testudines: Pelomedusidae). Acta Amazon 12(2):323–326
Allendorf FW, England PR, Luikart G et al (2008) Genetic effects of harvest on wild animal populations. Trends Ecol Evol 23:327–337
Balkenhol N, Cushman SA, Storfer A et al (2016) Introduction to landscape genetics—concepts, methods, applications. In: Balkenhol N, Cushman SA, Storfer A et al (eds) Landscape genetics: concepts, methods, applications, vol 1. Wiley, West Sussex, pp 1–8
Batistella AM, Vogt RC (2008) Nesting ecology of Podocnemis erythrocephala (Testudines, Podocnemididae) of the Rio Negro, Amazonas, Brazil. Chelonian Conserv Biol 7:12–20
Beheregaray LB, Cooke G, Chao N et al (2015) Ecological speciation in the tropics: insights from comparative genetic studies in Amazonia. Front Genet 5:1–19
Bermudez-Romero AL, Castelblanco-Martínez N, Bernhard R et al (2015) Nesting habitat of the ‘cupiso’ Podocnemis sextuberculata (Testudines: Podocnemididae) in Erepecu Lake (Pará-Brazil). Acta Biol Colomb 20:183–191
Bernardes VCD, Ferrara CR, Vogt RC et al (2014) Abundance and population structure of Podocnemis erythrocephala (Testudines, Podocnemididae) in the Unini River, Amazonas. Chelonian Conserv Biol 13:89–95
Bernhard R (2010) Dinâmica populacional de Podocnemis erythrocephala, no rio Ayuanã, Amazonas, Brasil. PhD Thesis, INPA
Burnham KP, Anderson DR (2003) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New York
Caldera EJ, Bolnick DI (2008) Effects of colonization history and landscape structure on genetic variation within and among threespine stickleback (Gasterosteus aculeatus) populations in a single watershed. Evol Ecol Res 10:575–598
Cantanhede A, Da Silva V, Ferreira M et al (2005) Phylogeography and population genetics of the endangered Amazonian manatee, Trichechus inunguis Natterer, 1883 (Mammalia, Sirenia). Mol Ecol 14:401–413
Carnaval AC, Hickerson MJ, Haddad CF, Rodrigues MT, Moritz C (2009) Stability predicts genetic diversity in the Brazilian Atlantic forest hotspot. Science 323:785–789
Carnaval AC, Waltari E, Rodrigues MT et al (2014) Prediction of phylogeographic endemism in an environmentally complex biome. Proc R Soc Lond B Biol Sci 281:20141461
Conway-Gómez K (2007) Effects of human settlements on abundance of Podocnemis unifilis and P. expansa turtles in northeastern Bolivia. Chelonian Conserv Biol 6:199–205
Cook BD, Kennard MJ, Real K et al (2011) Landscape genetic analysis of the tropical freshwater fish Mogurnda mogurnda (Eleotridae) in a monsoonal river basin: importance of hydrographic factors and population history. Freshw Biol 56:812–827
Corander J, Marttinen P, Sirén J et al (2006) BAPS: Bayesian analysis of population structure, Manual v. 4.1. Department of Mathematics, University of Helsinki
Davis CD, Epps CW, Flitcroft RL, Banks MA (2018) Refining and defining riverscape genetics: how rivers influence population genetic structure. WIREs Water 5:e1269
De Thoisy B, Hrbek T, Farias IP et al (2006) Genetic structure, population dynamics, and conservation of Black caiman (Melanosuchus niger). Biol Conserv 133:474–482
Dileo MF, Wagner HH (2016) A landscape ecologist’s agenda for landscape genetics. Curr Lands Ecol Rep 1:115–126
Domisch S, Amatulli G, Jetz W (2015) Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution. Sci Data 2:1–13
Ellegren H, Galtier N (2016) Determinants of genetic diversity. Nat Rev Genet 17:422–433
Epps CW, Keyghobadi N (2015) Landscape genetics in a changing world: disentangling historical and contemporary influences and inferring change. Mol Ecol 24:6021–6040
Epps CW, Palsbøll PJ, Wehausen JD et al (2005) Highways block gene flow and cause a rapid decline in genetic diversity of desert bighorn sheep. Ecol Lett 8:1029–1038
Escalona T, Engstrom TN, Hernandez O et al (2009) Population genetics of the endangered South American freshwater turtle, Podocnemis unifilis, inferred from microsatellite DNA data. Conserv Genet 10:1683–1696
Excoffier L, Lischer HE (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 10:564–567
Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131:479–491
Fachin-Terán A, Vogt RC (2014) Alimentación de Podocnemis Sextuberculata (Testudines: Podocnemididae) en la Reserva Mamirauá, Amazonas, Brasil. Revista Colombiana de Ciencia Animal 6:286–309
Fachín-Terán A, Vogt RC, Thorbjarnarson JB (2006) Seasonal movements of Podocnemis sextuberculata (Testudines: Podocnemididae) in the Mamirauá Sustainable Development Reserve, Amazonas, Brazil. Chelonian Conserv Biol 5:18–24
Fagundes CK, Vogt RC, De Marco JP (2015) Testing the efficiency of protected areas in the Amazon for conserving freshwater turtles. Divers Distrib 22:123–135
Fantin C, Farias I, Monjeló L et al (2010) Polyandry in the red-headed river turtle Podocnemis erythrocephala (Testudines, Podocnemididae) in the Brazilian Amazon. Genet Mol Res 9:435–440
Fantin C, Pereira DIM, Ferreira JF et al (2015) Evidence of multiple paternal contribution in Podocnemis sextuberculata (Testudines: Podocnemididae) detected by microsatellite markers. Phyllomedusa 14:89–97
Ferrara CR, Fagundes CK, Morcatty TQ et al (2017) Quelônios Amazônicos: Guia de identificação e distribuição. Editora INPA, Manaus
Ferrier S, Manion G, Elith J et al (2007) Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Divers Distrib 13:252–264
Fitzpatrick MC, Keller SR (2015) Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol Lett 18:1–16
Fitzpatrick MC, Sanders NJ, Normand S et al (2013) Environmental and historical imprints on beta diversity: insights from variation in rates of species turnover along gradients. Proc R Soc Lond B Biol Sci 280:20131201
Frankham R (1996) Relationship of genetic variation to population size in wildlife. Conserv Biol 10:1500–1508
Gomez-Uchida D, Knight TW, Ruzzante DE (2009) Interaction of landscape and life history attributes on genetic diversity, neutral divergence and gene flow in a pristine community of salmonids. Mol Ecol 18:4854–4869
Goodall-Copestake W, Tarling G, Murphy E (2012) On the comparison of population-level estimates of haplotype and nucleotide diversity: a case study using the gene cox1 in animals. Heredity 109:50–56
Graham CH, Moritz C, Williams SE (2006) Habitat history improves prediction of biodiversity in rainforest fauna. PNAS 103(3):632–636
Gravena W, Silva VM, Silva MN et al (2015) Living between rapids: genetic structure and hybridization in botos (Cetacea: Iniidae: Inia spp.) of the Madeira River, Brazil. Biol J Linn Soc 114:764–777
Hand BK, Muhlfeld CC, Wade AA et al (2015) Climate variables explain neutral and adaptive variation within salmonid metapopulations: the importance of replication in landscape genetics. Mol Ecol 25:689–705
Hayes FE, JaN S (2004) The Amazon River as a dispersal barrier to passerine birds: effects of river width, habitat and taxonomy. J Biogeogr 31:1809–1818
Hijmans R, Cameron S, Parra J et al (2005) WorldClim, version 1.3. University of California, Berkeley
Hijmans RJ, Phillips S, Leathwick J et al (2015) dismo: Species distribution modeling. R package version 1.0-12
Hrbek T, Farias IP, Crossa M et al (2005) Population genetic analysis of Arapaima gigas, one of the largest freshwater fishes of the Amazon basin: implications for its conservation. Anim Conserv 8:297–308
Hughes JM, Schmidt DJ, Finn DS (2009) Genes in streams: using DNA to understand the movement of freshwater fauna and their riverine habitat. BioScience 59(7):573–583
Jenkins DG, Carey M, Czerniewska J et al (2010) A meta-analysis of isolation by distance: Relic or reference standard for landscape genetics? Ecography 33:315–320
Junk WJ, Piedade MTF, Schöngart J et al (2011) A classification of major naturally-occurring Amazonian lowland wetlands. Wetlands 31:623–640
Kanno Y, Vokoun JC, Letcher BH (2011) Fine-scale population structure and riverscape genetics of brook trout (Salvelinus fontinalis) distributed continuously along headwater channel networks. Mol Ecol 20:3711–3729
Kovach RP, Muhlfeld CC, Wade AA et al (2015) Genetic diversity is related to climatic variation and vulnerability in threatened bull trout. Glob Change Biol 21:2510–2524
Kuo C, Janzen FJ (2004) Genetic effects of a persistent bottleneck on a natural population of ornate box turtles (Terrapene ornata). Conserv Genet 5:425–437
Librado P, Rozas J (2009) DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25:1451–1452
Liggins L, Treml EA, Possingham HP et al (2016) Seascape features, rather than dispersal traits, predict spatial genetic patterns in co-distributed reef fishes. J Biogeogr 43:256–267
Malhi Y, Baker TR, Phillips OL et al (2004) The above-ground coarse wood productivity of 104 Neotropical forest plots. Glob Change Biol 10:563–591
Manel S, Schwartz MK, Luikart G et al (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18:189–197
Manion G, Lisk M, Ferrier S et al (2016) gdm: Functions for Generalized Dissimilarity Modeling. R package.
Marsack K, Swanson BJ (2009) A genetic analysis of the impact of generation time and road-based habitat fragmentation on eastern box turtles (Terrapene c. carolina). Copeia 2009:647–652
Martin AP, Palumbi SR (1993) Body size, metabolic rate, generation time, and the molecular clock. Proc Natl Acad Sci USA 90:4087–4091
Mazerolle MJ, Mazerolle MMJ (2016) Package ‘AICcmodavg’
Mcrae BH (2006) Isolation by resistance. Evolution 60:1551–1561
Mitchell MW, Locatelli S, Clee PRS et al (2015) Environmental variation and rivers govern the structure of chimpanzee genetic diversity in a biodiversity hotspot. BMC Evol Biol 15:1–13
Mittermeier RA, Vogt RC, Bernhard R et al (2015) Podocnemis erythrocephala (Spix 1824)—Red-headed Amazon River Turtle, Irapuca. In: Rhodin A, Pritchard P, Van Dijk P, et al (eds) Conservation biology of freshwater turtles and tortoises: a compilation project of the IUCN/SSC Tortoise and Freshwater Turtle Specialist Group, Chelonian Research Monographs, vol 5, pp 087.081-010
Moore J, Miller H, Daugherty C et al (2008) Fine-scale genetic structure of a long-lived reptile reflects recent habitat modification. Mol Ecol 17:4630–4641
Murphy M, Evans JS (2011) Genetic patterns as a function of landscape process: applications of neutral genetic markers for predictive modeling in landscape ecology. In: Drew CA, Wiersma YF, Huettmann F (eds) Predictive species and habitat modeling in landscape ecology. Springer, New York, pp 161–188
Murphy M, Dezzani R, Pilliod D et al (2010) Landscape genetics of high mountain frog metapopulations. Mol Ecol 19:3634–3649
NASA (2016) Moderate Resolution Imaging Spectroradiometer (MODIS). National Aeronautics and Space Administration. Available from https://modis.gsfc.nasa.gov. Accessed Dec 2016.
Nei M (1987) Molecular evolutionary genetics. Columbia University Press, New York
Ortego J, Gugger PF, Sork VL (2015) Climatically stable landscapes predict patterns of genetic structure and admixture in the Californian canyon live oak. J Biogeogr 42:328–338
Ouellet-Cauchon G, Mingelbier M, Lecomte F et al (2014) Landscape variability explains spatial pattern of population structure of northern pike (Esox lucius) in a large fluvial system. Ecol Evol 4:3723–3735
Ozerov MY, Veselov AE, Lumme J et al (2012) “Riverscape” genetics: river characteristics influence the genetic structure and diversity of anadromous and freshwater Atlantic salmon (Salmo salar) populations in northwest Russia. Can J Fish Aquat Sci 69:1947–1958
Pantoja-Lima J, Juárez CBP, Teixeira A et al (2009) Seleção de locais de desova e sobrevivência de ninhos de quelônios Podocnemis no baixo Rio Purus, Amazonas, Brasil. Revista Colombiana de Ciencia Animal 1:37–59
Pantoja-Lima J, Aride PH, De Oliveira AT et al (2014) Chain of commercialization of Podocnemis spp. turtles (Testudines: Podocnemididae) in the Purus River, Amazon basin, Brazil: current status and perspectives. J Ethnobiol Ethnomed 10:8
Paz-Vinas I, Blanchet S (2015) Dendritic connectivity shapes spatial patterns of genetic diversity: a simulation-based study. J Evol Biol 28:986–994
Paz-Vinas I, Loot G, Stevens V et al (2015) Evolutionary processes driving spatial patterns of intra-specific genetic diversity in river ecosystems. Mol Ecol 24:4586–4604
Pearse DE, Arndt AD, Valenzuela N et al (2006) Estimating population structure under nonequilibrium conditions in a conservation context: continent-wide population genetics of the giant Amazon River turtle, Podocnemis expansa (Chelonia; Podocnemididae). Mol Ecol 15:985–1006
Peres CA (2000) Effects of subsistence hunting on vertebrate community structure in Amazonian forests. Conserv Biol 14:240–253
Pezzuti JC, Lima JP, Da Silva DF et al (2010) Uses and taboos of turtles and tortoises along Rio Negro, Amazon Basin. J Ethnobiol 30:153–168
Reid BN, Mladenoff DJ, Peery MZ (2017) Genetic effects of landscape, habitat preference, and demography on three co-occurring turtle species. Mol Ecol 26:781–798
Richardson JL (2012) Divergent landscape effects on population connectivity in two co-occurring amphibian species. Mol Ecol 21:4437–4451
Salzburger W, Ewing GB, Von Haeseler A (2011) The performance of phylogenetic algorithms in estimating haplotype genealogies with migration. Mol Ecol 20:1952–1963
Santos RC, Viana MNS, LaS M et al (2016) Testing the effects of barriers on the genetic connectivity in Podocnemis erythrocephala (Red-headed Amazon River Turtle): implications for management and conservation. Chelonian Conserv Biol 15:12–22
Schneider L, Ferrara CR, Vogt RC, Burger J (2011) History of turtle exploitation and management techniques to conserve turtles in the Rio Negro Basin of the Brazilian Amazon. Chelonian Conserv Biol 10(1):149–157
Selkoe KA, Scribner KT, Galindo HM (2016) Waterscape genetics—applications of landscape genetics to rivers, lakes, and seas. In: Balkenhol N, Cushman SA, Storfer A et al (eds) Landscape genetics: concepts, methods, applications, vol 1. Wiley, West Sussex, pp 220–246
Silva-Junior UL (2015) Análise dos extremos hidrológicos da bacia Amazônica e modelagem integrada (SNAP/Western Amazon-February 2015)
Sioli H (1984) The Amazon: limnology and landscape ecology of a mighty tropical river and its basin. Springer, Dordrecht
Smith NJ (1979) Aquatic turtles of Amazonia: an endangered resource. Biol Conserv 16:165–176
Sork VL, Waits L (2010) Contributions of landscape genetics—approaches, insights, and future potential. Mol Ecol 19:3489–3495
Spear SF, Cushman SA, Mcrae BH (2016) Resistance surface modeling in landscape genetics. In: Balkenhol N, Cushman SA, Storfer A et al (eds) Landscape genetics: concepts, methods, applications, vol 1. Wiley, West Sussex, pp 129–148
Spielman D, Brook BW, Frankham R (2004) Most species are not driven to extinction before genetic factors impact them. Proc Natl Acad Sci USA 101:15261–15264
Stamatakis A (2006) RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22:2688–2690
Steele C, Baumsteiger J, Storfer A (2009) Influence of life-history variation on the genetic structure of two sympatric salamander taxa. Mol Ecol 18:1629–1639
Storfer A, Murphy MA, Spear SF et al (2010) Landscape genetics: Where are we now? Mol Ecol 19:3496–3514
Thomaz AT, Malabarba LR, Bonatto SL et al (2015) Testing the effect of palaeodrainages versus habitat stability on genetic divergence in riverine systems: study of a Neotropical fish of the Brazilian coastal Atlantic Forest. J Biogeogr 42:2389–2401
Turtle Conservation Fund (2002) A global action plan for conservation of tortoises and freshwater turtles: strategy and funding prospectus 2002–2007. Conservation International and Chelonian Research Foundation, Washington
Van Etten J (2012) gdistance: Distances and routes on geographical grids. R package version 1.1–4
Vargas-Ramírez M, Stuckas H, Castaño-Mora OV et al (2012) Extremely low genetic diversity and weak population differentiation in the endangered Colombian river turtle Podocnemis lewyana (Testudines: Podocnemididae). Conserv Genet 13:65–77
Venticinque E, Forsberg B, Barthem R et al (2016) An explicit GIS-based river basin framework for aquatic ecosystem conservation in the Amazon. Earth Syst Sci Data 8:651
Viana MNS, Oliveira JA, Agostini MA et al (2017) Population genetic structure of the threatened Amazon River turtle Podocnemis sextuberculata (Testudines, Podocnemididae). Chelonian Conserv Biol 16(2):128–138
Vogt RC (2008) Amazon turtles. INPA, Manaus
Wagner HH, Fortin M-J (2013) A conceptual framework for the spatial analysis of landscape genetic data. Conserv Genet 14:253–261
Wagner HH, Fortin MJ (2016) Basics of spatial data analysis: linking landscape and genetic data for landscape genetic studies. In: Balkenhol N, Cushman SA, Storfer A et al (eds) Landscape genetics: concepts, methods, applications, vol 1. Wiley, West Sussex, pp 77–98
Wang IJ (2010) Recognizing the temporal distinctions between landscape genetics and phylogeography. Mol Ecol 19:2605–2608
Wang IJ, Bradburd GS (2014) Isolation by environment. Mol Ecol 23:5649–5662
Wang Y-H, Yang K-C, Bridgman CL, Lin L-K (2008) Habitat suitability modelling to correlate gene flow with landscape connectivity. Landsc Ecol 23:989–1000
Wang IJ, Savage WK, Bradley Shaffer H (2009) Landscape genetics and least-cost path analysis reveal unexpected dispersal routes in the California tiger salamander (Ambystoma californiense). Mol Ecol 18:1365–1374
Wang IJ, Glor RE, Losos JB (2013) Quantifying the roles of ecology and geography in spatial genetic divergence. Ecol Lett 16:175–182
Wofford JE, Gresswell RE, Banks MA (2005) Influence of barriers to movement on within-watershed genetic variation of coastal cutthroat trout. Ecol Appl 15:628–637
Wright S (1931) Evolution in Mendelian populations. Genetics 16:97–159
Wright S (1943) Isolation by distance. Genetics 28:114
Wright DH (1983) Species-energy theory: an extension of species-area theory. Oikos 496–506
Zeller KA, Mcgarigal K, Whiteley AR (2012) Estimating landscape resistance to movement: a review. Landsc Ecol 27:777–797
Acknowledgements
This work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq (Master’s fellowship to J.A.O., 475559/2013-4 and 305535/2017-0 to F.P.W., 302297/2015-4 to G.C.C., SISBIOTA 563348/2010-0 to I.P.F.); Fundação de Amparo à Pesquisa do Amazonas-FAPEAM (062.00665/2015 and 062.01110/2017 to F.P.W.); Partnerships for Enhanced Engagement in Research from the U.S. National Academy of Sciences and U.S. Agency of International Development-PEER NAS/USAID (AID-OAA-A-11-00012 to F.P.W.); and by the L’Oréal-UNESCO For Women In Science Program to F.P.W. We thank M. N. S. Viana for contributing with additional biological samples used in this work. We also thank P. C. A. Machado, R. C. Vogt, J. Erickson and F. Fernandes for collecting samples. The authors declare no conflicts of interest.
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Oliveira, J.d.A., Farias, I.P., Costa, G.C. et al. Model-based riverscape genetics: disentangling the roles of local and connectivity factors in shaping spatial genetic patterns of two Amazonian turtles with different dispersal abilities. Evol Ecol 33, 273–298 (2019). https://doi.org/10.1007/s10682-019-09973-4
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DOI: https://doi.org/10.1007/s10682-019-09973-4