Skip to main content

Advertisement

Log in

Regional replication of landscape genetics analyses of the Mississippi slimy salamander, Plethodon mississippi

  • Research Article
  • Published:
Landscape Ecology Aims and scope Submit manuscript

Abstract

Context

Landscape genetics can identify habitat features that facilitate or resist gene flow, providing a framework for anticipating the impacts of land use changes on dispersal of individuals. To inform management, a better understanding of how inferences derived from one study region are applicable to other regions is needed.

Objectives

We investigated the manner in which five landscape variables correlated with gene flow among Plethodon mississippi populations in two study regions. We compared order of importance, direction (facilitation vs. resistance of gene flow) and scale of effect, and functional relationships of variables within each study area.

Methods

In forests in Mississippi and Alabama, USA, we tested individual-based genetic distances derived from microsatellite genotypes against effective distances caused by agriculture, hardwoods, pine, manmade structures, and wetlands that were optimized for both scale and transformation using maximum likelihood population effects modeling.

Results

Of the landscape variables, agriculture and wetlands ranked at the top of both study areas’ models. In both forest regions, agriculture was consistently associated with resistance, whereas pine was inferred to facilitate gene flow. However, we found region-specific differences in effects of wetlands, hardwoods, and manmade structures. Configuration of the latter landscape variables differed between forest regions, which may explain the contrasting outcomes.

Conclusions

Our results underscore the value of metareplication in revealing which components of landscape genetics models may be consistent across different portions of a species’ range, and those that have context-dependent impacts on gene flow. We also highlight the need to consider habitat configuration when interpreting the results of landscape genetics analyses.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data avalaibility

Genotypic data are available from DRYAD Repository entry https://doi.org/10.5061/dryad.h18931zgh.

References

  • Adamack AT, Gruber B (2014) PopGenReport: simplifying basic population genetic analyses in R. Methods Ecol Evol 5:384–387

    Article  Google Scholar 

  • Allendorf FW, Luikart G, Aitken SN (2013) Conservation and the genetics of populations, 2nd edn. Wiley-Blackwell, New Jersey

    Google Scholar 

  • Bailey LL, Simons TR, Pollock KHZ (2004) Estimating detection probability parameters for plethodon salamanders using the robust capture-recapture design. J Wildl Manag 68:1–13

    Article  Google Scholar 

  • Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lem4. J Stat Softw 67:1–48

    Article  Google Scholar 

  • Beier P, Majka DR, Spencer WD (2008) Forks in the road: choices in procedures for designing wildland linkages. Conserv Biol 22:836–851

    Article  PubMed  Google Scholar 

  • Berry O, Tocher MD, Sarre SD (2004) Can assignment tests measure dispersal? Mol Ecol 13:551–561

    Article  PubMed  Google Scholar 

  • Cabe PR, Page RB, Hanlon TJ, Aldrich ME, Connors L, Marsh DM (2007) Fine-scale population differentiation and gene flow in a terrestrial salamander (Plethodon cinereus) living in continuous habitat. Hered 98:53–60

    Article  CAS  Google Scholar 

  • Castillo JA, Epps CW, Jeffrees MR, Ray C, Rodhouse TJ, Schwalm D (2016) Replicated landscape genetic and network analyses reveal wide variation in functional connectivity for American pikas. Ecol Appl 26:1660–1676

    Article  PubMed  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 3:361

    Article  Google Scholar 

  • Cleary KA, Waits LP, Finegan B (2017) Comparative landscape genetics of two frugivorous bats in a biological corridor undergoing agricultural intensification. Mol Ecol 26:4603–4617

    Article  PubMed  Google Scholar 

  • Connette GM, Semlitsch RD (2015) A multistate mark-recapture approach to estimating survival of PIT-tagged salamanders following timber harvest. J Appl Ecol 52:1316–1324

    Article  Google Scholar 

  • Costanzi J-M, Mège P, Boissinot A, Isselin-Nondedeu F, Guérin S, Lourdais O, Trochet A, Le Petitcorps Q, Legrand A, Varenne F, Grillet P, Morin-Pinaud S, Picard D (2018) Agricultural landscapes and the Loire River influence the genetic structure of the marbled newt in Western France. Sci Rep 8:14177

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Coulon A, Cosson JF, Angibault JM, Cargnelutti B, Galan M, Morellet N, Petit E, Aulagnier S, Hewison JM (2004) Landscape connectivity influences gene flow in a roe deer population inhabiting a fragmented landscape: an individual-based approach. Mol Ecol 13:2841–2850

    Article  CAS  PubMed  Google Scholar 

  • Cushman SA (2006) Effects of habitat loss and fragmentation on amphibians: a review and prospectus. Biol Conserv 128:231–240

    Google Scholar 

  • Cushman SA, Raphael MG, Ruggiero LF, Shirk AS, Wasserman TN, O’Doherty EC (2011) Limiting factors and landscape connectivity: the American marten in the Rocky Mountains. Landsc Ecol 26:1137–1149

    Article  Google Scholar 

  • Cushman SA, Shirk AJ, Landguth EL (2013) Landscape genetics and limiting factors. Conserv Genet 14:263–274

    Article  Google Scholar 

  • Dray S, Dufour AB (2007) The ade4 package: implementing the duality diagram for ecologists. J Stat Softw 22:1–20

    Article  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 

  • Emel SL, Storfer A (2012) A decade of amphibian population genetic studies: synthesis and recommendations. Conserv Genet 13:1685–1689

    Article  Google Scholar 

  • Epps CW, Wasser SK, Keim JL, Mutayoba BM, Brashares JS (2013) Quantifying past and present connectivity illuminates a rapidly changing landscapefor the African elephant. Mol Ecol 22:1574–1588

    Article  PubMed  Google Scholar 

  • Epps CW, Crowhurst RS, Nickerson BS (2018) Assessing changes in functional connectivity in a desert bighorn sheep metapopulation after two generations. Mol Ecol 27:2334–2346

    Article  PubMed  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 

  • Fahrig L, Merriam G (1985) Habitat patch connectivity and population survival. Ecol 66:1762–1768

    Article  Google Scholar 

  • Galpern P, Manseau M, Wilson P (2012) Grains of connectivity: analysis at multiple spatial scales in landscape genetics. Mol Ecol 21:3996–4009

    Article  PubMed  Google Scholar 

  • Gilpin ME, Soulé ME (1986) Minimum viable populations: processes of extinction. Conservation biology: the science of scarcity and diversity. Sinauer Associates, Sunderland, pp 19–34

    Google Scholar 

  • Hebblewhite M, Haydon DT (2010) Distinguishing technology from biology: a critical review of the use of GPS telemetry data in ecology. Philos Trans R Soc B 365:2303–2312

    Article  Google Scholar 

  • Highton R (1962) Geographic variation in the life history of the slimy salamander. Copeia 1962:597–613

    Article  Google Scholar 

  • Highton R (1989) Biochemical evolution in the slimy salamanders of the Plethodon glutinosus complex in the eastern United States. Part 1. Geographic protein variation. Ill. Biol Monogr 57:1–78

    Google Scholar 

  • Hurvich CM, Tsai C-L (1989) Regression and time series model selection in small samples. Biometrika 76:297–307

    Article  Google Scholar 

  • Johnson DH (2002) The importance of replication in wildlife research. J Wildl Manag 66:919–932

    Article  Google Scholar 

  • Jones KS, Weisrock DW (2018) Genomic data reject the hypothesis of sympatric ecological speciation in a clade of Desmognathus salamanders. Evolution 72:2378–2393

    Article  PubMed  Google Scholar 

  • Jules ES, Shahani P (2003) A broader ecological context to habitat fragmentation: why matrix habitat is more important than we thought. J Veg Sci 14:459–464

    Article  Google Scholar 

  • Keeley ATH, Beier P, Keeley BW, Fagan ME (2017) Habitat suitability is a poor proxy for landscape connectivity during dispersal and mating movements. Landsc Urban Plan 161:90–102

    Article  Google Scholar 

  • Keller D, Holderegger R, van Strien MJ, Bolliger J (2014) How to make landscape genetics beneficial for conservation management? Conserv Genet 16:503–512

    Article  Google Scholar 

  • Keyghobadi N (2007) The genetic implications of habitat fragmentation for animals. Can J Zool 85:1049–1064

    Article  Google Scholar 

  • Koenig WD, Van Vuren D, Hooge PN (1996) Detectability, philopatry, and the distribution of dispersal distances in vertebrates. Trends Ecol Evol 11:514–517

    Article  CAS  PubMed  Google Scholar 

  • Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18:189–197

    Article  Google Scholar 

  • Marsh DM, Page RB, Hanlon TJ, Corritone R, Little EC, Seifert DE, Cabe PR (2008) Effects of roads on patterns of genetic differentiation in red-backed salamanders, Plethodon cinereus. Conserv Genet 9:603–613

    Article  Google Scholar 

  • Mayor SJ, Schneider DC, Schaefer JA, Mahoney SP (2009) Habitat selection at multiple scales. Écoscience 16:238–247

    Article  Google Scholar 

  • McGarigal K, Cushman SA, Ene E (2012) FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. http://www.umass.edu/landeco/research/fragstats/fragstats.html

  • McGarigal K, Wan HY, Zeller KA et al (2016) Multi-scale habitat selection modeling: a review and outlook. Landsc Ecol 31:1161–1175

    Article  Google Scholar 

  • Moseley KR, Castleberry SB, Ford WM (2004) Coarse woody debris and pine litter manipulation effects on movement and microhabitat use of Ambystoma talpoideum in a Pinus taeda stand. For Ecol Manag 191:387–396

    Article  Google Scholar 

  • Ovaska K (1988) Spacing and movements of the salamander Plethodon vehiculum. Herpetologica 44:377–386

    Google Scholar 

  • Parisien MA, Miller C, Parks A, DeLanvey E, Robinne F, Flannigan M (2016) The spatially varying influence of humans on fire probability in North America. Environ Res Lett 11:075005

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  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

    Article  PubMed  Google Scholar 

  • Petranka JW (1998) Salamanders of the US and Canada. Smithsonian Institution, Washington DC

    Google Scholar 

  • Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959

    CAS  Google Scholar 

  • Prunier JG, Kaufman B, Léna JP, Fenet S, Pompanon F, Joly P (2014) A 40-year-old divided highway does not prevent gene flow in the alpine newt Ichthyosaura alpestris. Conserv Genet 15:453–468

    Article  Google Scholar 

  • Purrenhage JL, Niewiarowski PH, Moore FB-G (2009) Population structure of spotted salamanders (Ambystoma maculatum) in a fragmented landscape. Mol Ecol 18:235–247

    Article  CAS  PubMed  Google Scholar 

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

  • Raymond M, Rousset F (1995) GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Heredit 86:248–249

    Article  Google Scholar 

  • Reddy PA, Puyravaud JP, Cushman SA, Segu H (2019) Spatial variation in the response of tiger gene flow to landscape features and limiting factors. Anim Conserv

  • Richardson JL, Brady SP, Wang IJ, Spear SF (2016) Navigating the pitfalls and promise of landscape genetics. Mol Ecol 25:849–863

    Article  PubMed  Google Scholar 

  • Riecken U, Raths U (1996) Use of radio telemetry for studying dispersal and habitat use of Carabus coriaceus L. Ann Zool Fenn 33:109–116

    Google Scholar 

  • Riedel BL, Russell KR, Ford WM, O’Neill KP, Godwin HW (2008) Habitat relationships of eastern red-backed salamanders (Plethodon cinereus) in Appalachian agroforestry and grazing systems. Agric Ecosyst Environ 124:229–236

    Article  Google Scholar 

  • Row JR, Knick ST, Oyler-McCance SJ et al (2017) Developing approaches for linear mixed modeling in landscape genetics through landscape-directed dispersal simulations. Ecol Evol 7:3751–3761

    Article  PubMed  PubMed Central  Google Scholar 

  • Row JR, Oyler-McCance SJ, Fike JA, O’Donnell MS, Doherty KE, Aldridge CL, Bowen ZH, Fedy BC (2015) Landscape characteristics influencing the genetic structure of greater sage-grouse within the stronghold of their range: a holistic modeling approach. Ecol Evol 5:1955–1969

    Article  PubMed  PubMed Central  Google Scholar 

  • Salmerón M, Gbur EE, Bourland FM, Buehring NW, Earnest L, Fritschi FB, Golden BR, Hathcoat D, Lofton J, Thompson McClure A, Miller TD, Neely C, Shannon G, Udeigwe TK, Verbree DA, Vories ED, Wiebold WJ, Purcell LC (2016) Yield response to planting date among soybean maturity groups for irrigated production in the US Midsouth. Crop Sci 56:747–759

    Article  CAS  Google Scholar 

  • Schmitz OJ, Lawler JJ, Beier P, Groves C, Knight G, Boyce DA Jr, Bulluck J, Johnston KM, Klein ML, Muller K, Pierce DJ, Singleton WR, Stritthold JR, Theobald DM, Trombulak SC, Trainor A (2015) Conserving Biodiversity: Practical Guidance about Climate Change Adaptation Approaches in Support of Land-use Planning. Nat Area J 35:190–203

    Article  Google Scholar 

  • Shirk AJ, Landguth EL, Cushman SA (2017) A comparison of individual-based genetic distance metrics for landscape genetics. Mol Ecol Resour 17:1308–1317

    Article  CAS  PubMed  Google Scholar 

  • Shirk AJ, Landguth EL, Cushman SA (2018) A comparison of regression methods for model selection in individual-based landscape genetic analysis. Mol Ecol Resour 18:55–67

    Article  PubMed  Google Scholar 

  • Shirk AJ, Raphael MG, Cushman SA (2014) Spatiotemporal variation in resource selection: insights from the American marten (Martes americana). Ecol Appl 24:1434–1444

    Article  PubMed  Google Scholar 

  • Short Bull RA, Cushman SA, Mace R, Chilton T, Kendall KC, Landguth EL, Schwartz MK, McKelvey K, Allendorf FW, Luikart G (2011) Why replication is important in landscape genetics: american black bear in the Rocky Mountains. Mol Ecol 20:1092–1107

    Article  Google Scholar 

  • Smith WH, Rissler LJ (2010) Quantifying disturbance in terrestrial communities: abundance-Biomass Comparisons of herpetofauna closely track forest succession. Restor Ecol 18:195–204

    Article  Google Scholar 

  • Sork VL, Waits L (2010) Contributions of landscape genetics—approaches, insights, and future potential. Mol Ecol 19:3489–3495

    Article  PubMed  Google Scholar 

  • Spatola BN, Peterman WE, Stephens NT, Connette GM, Shepard DB, Kozak KH, Selmlitsch RD, Eggert LS (2013) Development of microsatellite loci for the western slimy salamander (Plethodon albagula) using 454 sequencing. Conserv Genet Resour 5:267–270

    Article  Google Scholar 

  • Sunnucks P (2000) Efficient genetic markers for population biology. Trends Ecol Evol 15:199–203

    Article  CAS  PubMed  Google Scholar 

  • Trainor AM, Walters JR, Morris WF, Sexton J, Moody A (2013) Empirical estimation of dispersal resistance surfaces: A case study with red-cockaded woodpeckers. Landsc Ecol 28:755–767

    Article  Google Scholar 

  • U.S. Department of Agriculture Forest Service (2004) National Forests in Alabama: revised land and resource management plan. Region 8, Atlanta, GA, Jan 2004

  • U.S. Department of Agriculture Forest Service (2012) National Forests in Mississippi: revised land and resource management plan. Region 8, Atlanta, GA, Aug 2014

  • van Etten J (2017) R package gdistance: distances and routes on geographical grids. J Stat Softw 76(13):07613

    Google Scholar 

  • Vergara M, Cushman SA, Ruiz-González A (2017) Ecological differences and limiting factors in different regional contexts: landscape genetics of the stone marten in the Iberian Peninsula. Landsc Ecol 32:1269–1283

    Article  Google Scholar 

  • Vitousek P, Mooney HA, Lubchenco J, Mellilo JM (1997) Human domination of Earth’s ecosystems. Science 227:494–499

    Article  Google Scholar 

  • Wan HY, McGarigal K, Ganey JL, Lauret V, Timm BC, Cushman SA (2017) Meta-replication reveals nonstationarity in multi-scale habitat selection ofMexican Spotted Owl. The Condor 119:641–658

    Article  Google Scholar 

  • Wang IJ (2009) Fine-scale population structure in a desert amphibian: landscape genetics of the black toad (Bufo exsul). Mol Ecol 18:3847–3856

    Article  PubMed  Google Scholar 

  • Zeller KA, McGarigal K, Whiteley AR (2012) Estimating landscape resistance to movement: a review. Landsc Ecol 27:777–797

    Article  Google Scholar 

  • Zeller KA, Vickers TW, Ernest HB, Boyce WM (2017) Multi-level, multi-scale resource selection functions and resistance surfaces for conservation planning: pumas as a case study. PLoS ONE 12:1–20

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was funded by the grants from the Birmingham Audubon Society and the University of Mississippi Graduate Student Council to S.M.B., and start-up funds from the University of Mississippi to R.C.G. This manuscript benefited from input of two anonymous reviewers and the editor. We thank C. Hyseni, B. Symula, and B. Stone for valuable discussion, and T. Breech, T. Burgess, and A. Burgess for assistance with fieldwork. This research was conducted under University of Mississippi IACUC Approval #15-020, Mississippi Department of Fish and Wildlife Permit #0324164, Alabama Department of Conservation and Natural Resources Permit #2017006899868680, and US Fish and Wildlife Service Permit #05012C. We also thank the US Forest Service for permission to collect specimens.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stephanie M. Burgess.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 41 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Burgess, S.M., Garrick, R.C. Regional replication of landscape genetics analyses of the Mississippi slimy salamander, Plethodon mississippi. Landscape Ecol 35, 337–351 (2020). https://doi.org/10.1007/s10980-019-00949-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10980-019-00949-x

Keywords

Navigation