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Effects of landscape features and flooding on the genetic structure of a small wetland rodent, the harvest mouse (Micromys minutus)

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Abstract

Context

The natural patchiness of wetlands and flooding events are likely to strongly affect the genetic structure of their terrestrial species. However, these effects are not well understood yet, especially for small mammals.

Objectives

We investigated at different spatial scales the genetic structure of the harvest mouse (Micromys minutus), a threatened small mammal strongly tied to wetlands, and the effects on gene flow of flooding and of the different types of landscape elements composing a wetland.

Methods

309 harvest mice were sampled in eight sites in Western Europe. Their genetic structure and diversity at 15 microsatellite loci were analyzed at a regional spatial scale and at a local scale, where the resistance of land cover and flooding to gene flow was also assessed, with the optimization procedure implemented in ResistanceGA.

Results

At a regional scale, our study revealed a strong genetic differentiation between populations from Northern Europe to the Mediterranean Sea. At the local scale, in a flooded wetland in France, the species exhibited a large genetic cluster over at least 45 km2, in spite of a large river crossing it. Winter floods explained genetic structure better than landscape features alone, with a stronger resistance to gene flow in reed beds where vegetation level above water was high: contrary to meadows, from which individuals are forced to escape, reed beds can be “golden prisons”, i.e. refuges during floods but with very low possibility of movements.

Conclusions

Several parameters influencing the functioning of a flooded population of harvest mouse are here highlighted that can also be useful for the development of plans to safeguard wetland ecosystems.

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References

  • Aplin K, Lunde D, Batsaikhan N, Kryštufek B, Meinig H, Henttonen H (2016) Micromys minutus. IUCN Red List of Threatened Species. http://www.iucnredlist.org. Accessed 04.01.2019.

  • Amori G, Contoli L, Nappi A (2008) Fauna d’Italia. Mammalia II, Calderini, Bologna

    Google Scholar 

  • Balčiauskas L, Balčiauskienė L, Janonytė A (2012) The influence of spring floods on small mammal communities in the Nemunas River Delta, Lithuania. Biologia (Bratisl) 67:1220–1229.

    Google Scholar 

  • Berckmoes V, Scheirs J, Jordaens K, Blust R, Backeljau T, Verhagen R (2005) Effects of environmental pollution on microsatellite DNA diversity in Wood mouse (Apodemus sylvaticus) populations. Environ Toxicol Chem 24:2898.

    CAS  PubMed  Google Scholar 

  • Berthier K, Galan M, Foltête JC, Charbonnel N, Cosson JF (2005) Genetic structure of the cyclic fossorial Water vole (Arvicola terrestris): landscape and demographic influences. Mol Ecol 14:2861–2871.

    CAS  PubMed  Google Scholar 

  • Blant M, Marchesi P, Descombes M, Capt S (2012) Nouvelles données sur la répartition de la souris des moissons (Micromys minutus Pallas, 1771) en Suisse occidentale et implications pour la gestion de son habitat. Rev Suisse Zool 119:485–500

    Google Scholar 

  • Booth W, Montgomery WI, Prodöhl PA (2009) Spatial genetic structuring in a vagile species, the European wood mouse. J Zool 279:219–228.

    Google Scholar 

  • Churchfield S, Hollier J, Brown VK (1997) Community structure and habitat use of small mammals in grasslands of different successional age. J Zool 242:519–530

    Google Scholar 

  • Clarke RT, Rothery P, Raybould AF (2002) Confidence limits for regression relationships between distance matrices: estimating gene flow with distance. J Agric Biol Environ Stat 7:361–372

    Google Scholar 

  • Coulon A, Fitzpatrick JW, Bowman J, Stith BM, Makarewich CA, Stenzler LM, Lovette IJ (2008) Congruent population structure inferred from dispersal behavior and intensive genetic surveys of the threatened Florida Scrub-Jay (Aphelocoma coerulescens). Mol Ecol 17:1685–1701

    CAS  PubMed  Google Scholar 

  • Cunnings A, Johnson E, Martin Y (2016) Fluvial seed dispersal of riparian trees: transport and depositional processes. Earth Surf Process Landf 41:615–625.

    Google Scholar 

  • Darinot F (2016) The Harvest mouse (Micromys minutus Pallas, 1771) as prey: a literature review. Folia Zool 65:117–137.

    Article  Google Scholar 

  • Darinot F (2019) Dispersion et structure génétique d’une population de Rat des moissons (Micromys minutus PALLAS, 1771) soumise à des inondations régulières. EPHE PSL research University Ph.D. thesis.

  • Després L, Henniaux C, Rioux D, Capblancq T, Zupan S, Čelik T, Sielezniew M, Bonato L, Ficetola GF (2019) Inferring the biogeography and demographic history of an endangered butterfly in Europe from multilocus markers. Biol J Linn Soc 126:95–113

    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.

    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.

    CAS  PubMed  Google Scholar 

  • Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. 22

  • Finlayson CM, D’Cruz R, Davidson N (2005) Ecosystems and human well-being: wetlands and water: synthesis. World Resources Institute, Washington, DC

    Google Scholar 

  • Fraaije RGA, Moinier S, van Gogh I, Timmers R, van Deelen JJ, Verhoeven JTA, Soons MB (2017) Spatial patterns of water-dispersed seed deposition along stream riparian gradients. PLoS ONE 12:e0185247.

    PubMed  PubMed Central  Google Scholar 

  • Frank F (1957) Zucht und Gefangenschafts-biologie der Zwergmaus (Micromys minutus subobscurus, Fritsche). Z Sáugetierk 22:1–44

    Google Scholar 

  • Garrido-Garduño T, Téllez-Valdés O, Manel S, Vázquez-Domínguez E (2016) Role of habitat heterogeneity and landscape connectivity in shaping gene flow and spatial population structure of a dominant rodent species in a tropical dry forest. J Zool 298:293–302.

    Google Scholar 

  • Gauffre B, Estoup A, Bretagnolle V, Cosson JF (2008) Spatial genetic structure of a small rodent in a heterogeneous landscape. Mol Ecol 17:4619–4629.

    CAS  PubMed  Google Scholar 

  • Gibbs JP (1993) Importance of small wetlands for the persistence of local populations of wetland-associated animals. Wetlands 13:25–31.

    Google Scholar 

  • Goudet J (2004) HIERFSTAT, a package for R to compute and test hierarchical F-statistics. Mol Ecol Notes 5:184–186.

    Google Scholar 

  • Harris S (1979) Breeding season, litter size and nestling mortality of the Harvest mouse, Micromys minutus (Rodentia: Muridae), in Britain. J Zool 188:437–442.

    Google Scholar 

  • Hartmann SA, Steyer K, Kraus RHS, Segelbacher G, Nowak C (2013) Potential barriers to gene flow in the endangered European wildcat (Felis silvestris). Conserv Genet 14:413–426.

    Google Scholar 

  • Heckel G, Burri R, Fink S, Desmet JF, Excoffier L (2005) Genetic structure and colonization processes in European populations of the Common vole, Microtus arvalis. Evolution 59:2231–2242.

    CAS  PubMed  Google Scholar 

  • Hesselbarth MHK, Sciaini M, With KA, Wiegand K, Nowosad J (2019) Landscapemetrics: an open-source R tool to calculate landscape metrics. Ecography 42:1648–1657.

    Google Scholar 

  • Jaarola M, Searle JB (2002) Phylogeography of Field voles (Microtus agrestis) in Eurasia inferred from mitochondrial DNA sequences: phylogeography of Field voles. Mol Ecol 11:2613–2621.

    CAS  PubMed  Google Scholar 

  • Janes JK, Miller JM, Dupuis JR, Malenfant RM, Gorrell JC, Cullingham CI, Andrew RL (2017) The K=2 conundrum. Mol Ecol 26:3594–3602.

    PubMed  Google Scholar 

  • Jiang S-Y, Lin YK (2009) Polymorphic Microsatellite Markers for the Harvest Mouse (Micromys minutus) in Taiwan. Taiwania 54:118–121

    Google Scholar 

  • Jombart T (2008) adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405.

    CAS  PubMed  Google Scholar 

  • Jombart T, Devillard S, Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet 11:94.

    PubMed  PubMed Central  Google Scholar 

  • Kalinowski ST (2005) hp-rare 1.0: a computer program for performing rarefaction on measures of allelic richness. Mol Ecol Notes 5:187–189.

    CAS  Google Scholar 

  • Kibbe WA (2007) OligoCalc: an online oligonucleotide properties calculator. Nucleic Acids Res 35:43–46.

    Google Scholar 

  • Klee RV, Mahoney AC, Christopher CC, Barrett GW (2004) Riverine peninsulas: an experimental approach to homing in White-footed Mice (Peromyscus leucopus). Am Midl Nat 151:408–413

    Google Scholar 

  • Kittlein MJ, Gaggiotti OE (2008) Interactions between environmental factors can hide isolation by distance patterns: a case study of Ctenomys rionegrensis in Uruguay. Proc R Soc B 275:2633–2638.

    PubMed  PubMed Central  Google Scholar 

  • Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I (2015) Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol Ecol Resour 15:1179–1191.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Lugon-Moulin N, Nner HB, Hausser J, Goudet JRM (1999) Do riverine barriers, history or introgression shape the genetic structuring of a Common shrew (Sorex araneus) population? Heredity 83:155–161

    PubMed  Google Scholar 

  • Malausa T, Gilles A, Meglécz E, Blanquart H, Duthoy S, Costedoat C, Dubut V, Pech N, Castagnone-Sereno P, Délye C, Feau N, Frey P, Gauthier P, Guillemaud T, Hazard L, Le Corre V, Lung-Escarmant B, Malé PJG, Ferreira S, Martin JF (2011) High-throughput microsatellite isolation through 454 GS-FLX titanium pyrosequencing of enriched DNA libraries: pyrosequencing of SSR-enriched DNA libraries. Mol Ecol Resour 11:638–644

    CAS  PubMed  Google Scholar 

  • Matocq MD, Patton JL, da Silva MNF (2000) Population genetic structure of two ecologically distinct Amazonian spiny rats: separating history and current ecology. Evolution 54:1423–1432

    CAS  PubMed  Google Scholar 

  • McRae BH, Beier P (2007) Circuit theory predicts gene flow in plant and animal populations. Proc Natl Acad Sci 104:19885–19890

    CAS  PubMed  PubMed Central  Google Scholar 

  • McRae BH, Dickson BG, Keitt TH, Shah VB (2008) Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89:2712–2724

    PubMed  Google Scholar 

  • Meirmans PG (2012) The trouble with isolation by distance. Mol Ecol 21:2839–2846

    PubMed  Google Scholar 

  • Mergey M, Bardonnet C, Quintaine T (2017) Identifying environmental drivers of spatial genetic structure of the European pine marten (Martes martes). Landsc Ecol 32:2261–2279.

    Google Scholar 

  • Patton JL, da Silva MNF, Malcolm JR (1994) Gene genealogy and differentiation among arboreal spiny rats (Rodentia: Echimyidae) of the Amazon basin: a test of the riverine barrier hypothesis. Evolution 48:1314–1323.

    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.

    Google Scholar 

  • Pereoglou F, Lindenmayer DB, MacGregor C, Ford F, Wood J, Banks SC (2013) Landscape genetics of an early successional specialist in a disturbance-prone environment. Mol Ecol 22:1267–1281.

    CAS  PubMed  Google Scholar 

  • Perez MF, Franco FF, Bombonato JR, Bonatelli IAS, Khan G, Romeiro-Brito M, Fegies AC, Ribeiro PM, Silva GAR, Moraes EM (2018) Assessing population structure in the face of isolation by distance: are we neglectingthe problem? Divers Distrib 24:1883–1889

    Google Scholar 

  • Perrow M, Jowitt A (1995) What future for the Harvest mouse? British Wildlife 6:356–365

    Google Scholar 

  • Peterman WE (2018) ResistanceGA: an R package for the optimization of resistance surfaces using genetic algorithms. Methods Ecol Evol 9:1638–1647.

    Google Scholar 

  • Peterman WE, Connette GM, Semlitsch RD, Eggert LS (2014) Ecological resistance surfaces predict fine-scale genetic differentiation in a terrestrial woodland salamander. Mol Ecol 23:2402–2413.

    PubMed  Google Scholar 

  • Porras-Hurtado L, Ruiz Y, Santos C, Phillips C, Carracedo A, Lareu MV (2013) An overview of STRUCTURE: applications, parameter settings, and supporting software. Front Genet. https://doi.org/10.3389/fgene.2013.00098

    Article  PubMed  PubMed Central  Google Scholar 

  • R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

  • Rocha RG, Ferreira E, Fonseca C, Justino J, Leite YLR, Pires Costa L (2014) Seasonal flooding regime and ecological traits influence genetic structure of two small rodents. Ecol Evol 4(24):4598–4608

    PubMed  PubMed Central  Google Scholar 

  • Rousset F (2008) genepop’007: a complete re-implementation of the genepop software for Windows and Linux. Mol Ecol Resour 8:103–106.

    PubMed  Google Scholar 

  • Russo IRM, Sole CL, Barbato M, von Bramann U, Bruford MW (2016) Landscape determinants of fine-scale genetic structure of a small rodent in a heterogeneous landscape (Hluhluwe-iMfolozi Park, South Africa). Sci Rep 6:29168

    CAS  PubMed  PubMed Central  Google Scholar 

  • Schooley RL, Branch LC (2009) Enhancing the area–isolation paradigm: habitat heterogeneity and metapopulation dynamics of a rare wetland mammal. Ecol Appl 19:1708–1722.

    PubMed  Google Scholar 

  • Schooley RL, Cosentino BJ (2018) Metapopulation dynamics of wetland species. In: Finlayson CM, Everard M, Irvine K et al (eds) The Wetland book. Springer, Netherlands, Dordrecht, pp 141–147

    Google Scholar 

  • Sheppe W, Haas P (1981) The annual cycle of small mammal populations along the Chobe River, Botswana. Mammalia 45:157–176.

    Google Scholar 

  • Smith KR, Barthman-Thompson L, Gould WR, Mabry KE (2014) Effects of natural and anthropogenic change on habitat use and movement of endangered Salt Marsh Harvest Mice. PLoS ONE 9:e108739.

    PubMed  PubMed Central  Google Scholar 

  • Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) micro-checker: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes 4:535–538.

    Google Scholar 

  • Weir BS, Cockerham CC (1984) Estimating F -statistics for the analysis of population structure. Evolution 38:1358–1370.

    CAS  PubMed  Google Scholar 

  • Wijnhoven S, Van der Velde G, Leuven RSEW, Smits AJM (2005) Flooding ecology of voles, mice and shrews: the importance of geomorphological and vegetational heterogeneity in river floodplains. Acta Theriol 50:453–472

    Google Scholar 

  • Wyttenbach A, Narain Y, Fredga K (1999) Genetic structuring and gene flow in a hybrid zone between two chromosome races of the Common shrew (Sorex araneus, Insectivora) revealed by microsatellites. Heredity 82:79–88.

    Google Scholar 

  • Zedler JB, Kercher S (2005) Wetland resources: status, trends, ecosystem services, and restorability. Annu Rev Environ Resour 30:39–74.

    Google Scholar 

  • Zhang M, Wang K, Wang Y, Guo C, Li B, Huang H (2007) Recovery of a rodent community in an agro-ecosystem after flooding. J Zool 272:138–147.

    Google Scholar 

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Acknowledgements

The authors would like to thank for people who help them in obtaining tissues of M. minutus for DNA isolation, for the description of new polymorphic microsatellite markers and genotyping of populations: Grégoire Massez and Sylvain Ceyte (Réserve Naturelle Nationale des Marais du Vigueirat), Gérard Hommay (Institut National de la Recherche Agronomique), François Léger (Office National de la Chasse et de la Faune Sauvage), Andrea Schüster and Katarina Foerster (University of Tübingen), Jacques Gilliéron and Hubert du Plessix (Prodon marsh), Maurice Benmergui (Office National de la Chasse et de la Faune Sauvage, Birieux pond), Olivier Glaizot (Cantonal Museum of Zoology of Lausanne, Neuchâtel lake), Emilie Wichroff and Rémi Bogey (Syndicat du Haut-Rhône, Saugey meander), Manuel Bouron (Conservatoire des Espaces Naturels de la Savoie, Chautagne marsh). This study was supported by the French Ministry of Environment and the Departmental Council of Ain.

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Darinot, F., Le Petitcorps, Q., Arnal, V. et al. Effects of landscape features and flooding on the genetic structure of a small wetland rodent, the harvest mouse (Micromys minutus). Landscape Ecol 36, 1755–1771 (2021). https://doi.org/10.1007/s10980-021-01235-5

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