Linking movement behavior and fine-scale genetic structure to model landscape connectivity for bobcats (Lynx rufus)

Springer Science and Business Media LLC - Tập 28 - Trang 471-486 - 2013
Dawn M. Reding1, Samuel A. Cushman2, Todd E. Gosselink3, William R. Clark1
1Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, USA
2US Forest Service Rocky Mountain Research Station, Flagstaff, USA
3Chariton Research Station, Iowa Department of Natural Resources, Chariton, USA

Tóm tắt

Spatial heterogeneity can constrain the movement of individuals and consequently genes across a landscape, influencing demographic and genetic processes. In this study, we linked information on landscape composition, movement behavior, and genetic differentiation to gain a mechanistic understanding of how spatial heterogeneity may influence movement and gene flow of bobcats in the agricultural landscape of Iowa (USA). We analyzed movement paths of 23 animals to parameterize landscape resistance surfaces, applied least cost path analysis to generate measures of effective geographic distance between DNA collection locations of 625 bobcats, and tested the correlation between genetic distance and the different models of geographic distance. We found that bobcats showed a strong preference for forest over any other habitat type, and that incorporating information on habitat composition both along the path and in the surrounding landscape provided the best model of movement. Measures of effective geographic distance were significantly correlated with genetic distance, but not once the effects of Euclidean distance were accounted for. Thus, despite the impact of habitat composition on movement behavior, we did not detect a signature of a landscape effect in genetic structure. Our results are consistent with the issue of limiting factors: the high uniformity of forest fragmentation across southern Iowa, the primary study area, results in a landscape resistance pattern virtually indistinguishable from the isolation-by-distance pattern. The northern portion of the state, however, is predicted to pose a high level of resistance to bobcat movement, which may impede the regional genetic connectivity of populations across the Midwest.

Tài liệu tham khảo

Abdi H, Williams LJ (2010) Jackknife. In: Salkind NJ, Dougherty DM, Frey B (eds) Encyclopedia of research design. Sage, Thousand Oaks, pp 655–660 Adriaensen F, Chardon JP, De Blust G, Swinnen E, Villalba S, Gulinck H, Matthysen E (2003) The application of ‘least-cost’ modelling as a functional landscape model. Landscape Urban Plan 64:233–247 Anderson CD, Epperson BK, Fortin MJ, Holderegger R, James PMA, Rosenberg MS, Scribner KT, Spear S (2010) Considering spatial and temporal scale in landscape-genetic studies of gene flow. Mol Ecol 19:3565–3575 Beier P, Majka DR, Spencer WD (2008) Forks in the road: choices in procedures for designing wildland linkages. Conserv Biol 22:836–851 Belsey DA, Kuh E, Welsch RE (1980) Regression diagnostics. Wiley, New York Boutin-Ganache I, Raposo M, Raymond M, Deschepper CF (2001) M13-tailed primers improve the readability and usability of microsatellite analyses performed with two different allele-sizing methods. Biotechniques 31:24–28 Bruggeman JE, Garrott RA, White PJ, Watson FGR, Wallen R (2007) Covariates affecting spatial variability in bison travel behavior in Yellowstone National Park. Ecol Appl 17:1411–1423 Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New York Carmichael LE, Clark W, Strobeck C (2000) Development and characterization of microsatellite loci from lynx (Lynx canadensis), and their use in other felids. Mol Ecol 9:2197–2198 Corander J, Siren J, Arjas E (2008) Bayesian spatial modeling of genetic population structure. Comput Stat 23:111–129 Coulon A, Cosson JF, Angibault JM, Cargnelutti B, Galan M, Morellet N, Petit E, Aulagnier S, Hewison AJ (2004) Landscape connectivity influences gene flow in a roe deer population inhabiting a fragmented landscape: an individual-based approach. Mol Ecol 13:2841–2850 Coulon A, Morellet N, Goulard M, Cargnelutti B, Angibault JM, Hewison AJM (2008) Inferring the effects of landscape structure on roe deer (Capreolus capreolus) movements using a step selection function. Landscape Ecol 23:603–614 Cushman SA, Landguth EL (2010a) Scale dependent inference in landscape genetics. Landscape Ecol 25:967–979 Cushman SA, Landguth EL (2010b) Spurious correlations and inference in landscape genetics. Mol Ecol 19:3592–3602 Cushman SA, Lewis JS (2010) Movement behavior explains genetic differentiation in American black bears. Landscape Ecol 25:1613–1625 Cushman SA, McKelvey KS, Hayden J, Schwartz MK (2006) Gene flow in complex landscapes: testing multiple hypotheses with causal modeling. Am Nat 168:486–499 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. Landscape Ecol 26:1137–1149 Cushman SA, Shirk A, Landguth EL (2012a) Separating the effects of habitat area, fragmentation and matrix resistance on genetic differentiation in complex landscapes. Landscape Ecol 27:369–380 Cushman SA, Shirk AJ, Landguth EL (2012b) Landscape genetics and limiting factors. Conserv Genet. doi: 10.1007/s10592-012-0396-0 Deems EF Jr, Pursley D (1978) North American furbearers: their management, research, and harvest status in 1976. International Association of Fish and Wildlife Agencies, Washington, DC Etherington TR (2011) Python based GIS tools for landscape genetics: visualizing genetic relatedness and measuring landscape connectivity. Method Ecol Evol 2:52–55 Fahrig L, Nuttle WK (2005) Population ecology in spatially heterogeneous environments. In: Lovette GM, Jones CG, Turner MG, Weathers KC (eds) Ecosystem function in heterogeneous landscapes. Springer, New York, pp 95–118 Faircloth BC, Reid A, Valentine T, Eo SH, Terhune TM, Glenn TC, Palmer WE, Nairn CJ, Carroll JP (2005) Tetranucleotide, trinucleotide, and dinucleotide loci from the bobcat (Lynx rufus). Mol Ecol Notes 5:387–389 Gillies CS, Hebblewhite M, Nielsen SE, Krawchuk MA, Aldridge CL, Frair JL, Saher DJ, Stevens CE, Jerde CL (2006) Application of random effects to the study of resource selection by animals. J Anim Ecol 75:887–898 Gosselink T, Roberts S, Clark W, Reding D, Linde S (2011) Distribution and population dynamics of bobcats in Iowa. Final Report. State Wildlife Grant T-1-R-14. Iowa Department of Natural Resources, Des Moines, Iowa Graves TA, Wasserman TN, Ribeiro MC, Landguth EL, Spear SF, Balkenhol N, Higgins CB, Fortin MJ, Cushman SA, Waits LP (2012) The influence of landscape characteristics and home-range size on the quantification of landscape-genetics relationships. Landscape Ecol 27:253–266 Hagerty BE, Nussear KE, Esque TC, Tracy CR (2011) Making molehills out of mountains: landscape genetics of the Mojave desert tortoise. Landscape Ecol 26:267–280 Hardy OJ, Vekemans X (2002) SPAGEDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2:618–620 Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley, New York Johnson SA, Walker HD, Hudson CM (2010) Dispersal characteristics of juvenile bobcats in south-central Indiana. J Wildlife Manage 74:379–385 Karieva PM, Shigesada N (1983) Analyzing insect movement as a correlated random walk. Oecologia 56:234–238 Keyghobadi N, Roland J, Strobeck C (1999) Influence of landscape on the population genetic structure of the alpine butterfly Parnassius smintheus (Papilionidae). Mol Ecol 8:1481–1495 Kimura M, Weiss GH (1964) The stepping stone model of population structure and the decrease of genetic correlation with distance. Genetics 49:561–576 Klug PE, Wisely SM, With KA (2011) Population genetic structure and landscape connectivity of the Eastern Yellowbelly Racer (Coluber constrictor flaviventris) in the contiguous tallgrass prairie of northeastern Kansas, USA. Landscape Ecol 26:281–294 Landguth EL, Cushman SA, Schwart MK, McKelvey KS, Murphy M, Luikart G (2010) Quantifying the lag time to detect barriers in landscape genetics. Mol Ecol 19:4179–4191 Latch EK, Boarman WI, Walde A, Fleischer RC (2011) Fine-scale analysis reveals cryptic landscape genetic structure in desert tortoises. PLoS ONE 6:e27794 Linde SA (2010) Predicting favorable habitat for bobcats (Lynx rufus) in Iowa. MS thesis, Iowa State University, Ames, IA Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18:189–197 Manly BFJ, McDonald LL, Thomas D (2002) Resource selection by animals: statistical design and analysis for field studies. Kluwer, Boston Mantel N (1967) Detection of disease clustering and a generalized regression approach. Cancer Res 27:209–220 Menotti-Raymond M, David VA, Lyons LA, Schäffer AA, Tomlin JF, Hutton MK, O’Brien SJ (1999) A genetic linkage map of microsatellites in the domestic cat (Felis catus). Genomics 57:9–23 Menotti-Raymond MA, David VA, Wachter LL, Butler JM, O’Brien SJ (2005) An STR forensic typing system for genetic individualization of domestic cat (Felis catus) samples. J Forensic Sci 50:1061–1070 Newby JR (2011) Puma dispersal ecology in the central Rocky Mountains. MS thesis, University of Montana, Missoula, MT Oksanen J, Blanchet FG, Kindt R, Legendre P, O'Hara RB, Simpson GL, Solymos P, Stevens MHH, Wagner H (2010) vegan: community ecology package. R package version 1.17-5. Available at http://CRAN.R-project.org/package=vegan Pullinger MG, Johnson CJ (2010) Maintaining or restoring connectivity of modified landscapes: evaluating the least-cost path model with multiple sources of ecological information. Landscape Ecol 25:1547–1560 Rice WR (1989) Analyzing tables of statistical tests. Evolution 43:223–225 Richard Y, Armstrong DP (2010) Cost distance modelling of landscape connectivity and gap-crossing ability using radio-tracking data. J Appl Ecol 47:603–610 Rousset F (2000) Genetic differentiation between individuals. J Evolution Biol 13:58–62 Rousset F (2008) GENEPOP’007: a complete re-implementation of the GENEPOP software for Windows and Linux. Mol Ecol Res 8:103–106 Schick RS, Loarie SR, Colchero F, Best BD, Boustany A, Conde DA, Halpin PN, Joppa LN, McClellan CM, Clark JS (2008) Understanding movement data and movement processes: current and emerging directions. Ecol Lett 11:1338–1350 Schwartz MK, Copeland JP, Anderson NJ, Squires JR, Inman RM, McKelvey KS, Pilgrim KL, Waits LP, Cushman SA (2009) Wolverine gene flow across a narrow climatic niche. Ecology 90:3222–3232 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 Sork VL, Waits L (2010) Contributions of landscape genetics—approaches, insights, and future potential. Mol Ecol 19:3489–3495 Spear SF, Balkenhol N, Fortin MJ, McRae BH, Scribner K (2010) Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis. Mol Ecol 19:3576–3591 Stevens VM, Verkenne C, Vandewoestijne S, Wesselingh RA, Baguette M (2006) Gene flow and functional connectivity in the natterjack toad. Mol Ecol 15:2333–2344 Storfer A, Murphy MA, Spear SF, Holderegger R, Waits LP (2010) Landscape genetics: where are we now? Mol Ecol 19:3496–3514 Taylor PD, Fahrig L, Henein K, Merriam G (1993) Connectivity is a vital element of landscape structure. Oikos 68:571–573 Tucker SA, Clark WR, Gosselink TE (2008) Space use and habitat selection by bobcats in the fragmented landscape of south-central Iowa. J Wildlife Manage 72:1114–1124 Turchin P (1998) Quantitative analysis of movement: measuring and modeling population redistribution in animals and plants. Sinauer Associates, Sunderland 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 Wasserman TN, Cushman SA, Schwartz MK, Wallin DO (2010) Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho. Landscape Ecol 25:1601–1612 Weir BS (1996) Genetic data analysis II. Sinauer Associates, Sunderland Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population-structure. Evolution 38:1358–1370 Wright S (1943) Isolation by distance. Genetics 28:114–138 Xian G, Homer C, Fry J (2009) Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods. Remote Sens Environ 113:1133–1147 Zeller KA, McGarigal K, Whitely AR (2012) Estimating landscape resistance to movement: a review. Landscape Ecol 27:777–797