Literature DB >> 12836834

Spatial autocorrelation analysis offers new insights into gene flow in the Australian bush rat, Rattus fuscipes.

Rod Peakall1, Monica Ruibal, David B Lindenmayer.   

Abstract

Dispersal is a fundamental process that influences the response of species to landscape change and habitat fragmentation. In an attempt to better understand dispersal in the Australian bush rat, Rattus fuscipes, we have combined a new multilocus autocorrelation method with hypervariable microsatellite genetic markers to investigate fine-scale (< or = 1 km) patterns of spatial distribution and spatial genetic structure. The study was conducted across eight trapping transects at four sites, with a total of 270 animals sampled. Spatial autocorrelation analysis of bush rat distribution revealed that, in general, animals occurred in groups or clusters of higher density (< or = 200 m across), with intervening gaps or lower density areas. Spatial genetic autocorrelation analysis, based on seven hypervariable microsatellite loci (He = 0.8) with a total of 80 alleles, revealed a consistent pattern of significant positive local genetic structure. This genetic pattern was consistent for all transects, and for adults and sub-adults, males and females. By testing for autocorrelation at multiple scales from 10 to 800 m we found that the extent of detectable positive spatial genetic structure exceeded 500 m. Further analyses detected significantly weaker spatial genetic structure in males compared with females, but no significant differences were detected between adults and sub adults. Results from Mantel tests and hierarchical AMOVA further support the conclusion that the distribution of bush rat genotypes is not random at the scale of our study. Instead, proximate bush rats are more genetically alike than more distant animals. We conclude that in bush rats, gene flow per generation is sufficiently restricted to generate the strong positive signal of local spatial genetic structure. Although our results are consistent with field data on animal movement, including the reported tendency for males to move further than females, we provide the first evidence for restricted gene flow in bush rats. Our study appears to be the first microsatellite-based study of fine-scale genetic variation in small mammals and the first to report consistent positive local genetic structure across sites, age-classes, and sexes. The combination of new forms of autocorrelation analyses, hypervariable genetic markers and fine-scale analysis (< 1 km) may thus offer new evolutionary insights that are overlooked by more traditional larger scaled (> 10 km) population genetic studies.

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Year:  2003        PMID: 12836834     DOI: 10.1111/j.0014-3820.2003.tb00327.x

Source DB:  PubMed          Journal:  Evolution        ISSN: 0014-3820            Impact factor:   3.694


  89 in total

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5.  Application of Inter-Simple Sequence Repeat Markers in the Analysis of Populations of the Chagas Disease Vector Triatoma infestans (Hemiptera, Reduviidae).

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6.  Evidence that disease-induced population decline changes genetic structure and alters dispersal patterns in the Tasmanian devil.

Authors:  S Lachish; K J Miller; A Storfer; A W Goldizen; M E Jones
Journal:  Heredity (Edinb)       Date:  2010-03-10       Impact factor: 3.821

7.  Urban landscape genomics identifies fine-scale gene flow patterns in an avian invasive.

Authors:  G W Low; B Chattopadhyay; K M Garg; M Irestedt; Pgp Ericson; G Yap; Q Tang; S Wu; F E Rheindt
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8.  Weak genetic structure indicates strong dispersal limits: a tale of two coral reef fish.

Authors:  John F H Purcell; Robert K Cowen; Colin R Hughes; Dean A Williams
Journal:  Proc Biol Sci       Date:  2006-06-22       Impact factor: 5.349

9.  Effect of anthropogenic landscape features on population genetic differentiation of Przewalski's gazelle: main role of human settlement.

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10.  Dispersal and population structure at different spatial scales in the subterranean rodent Ctenomys australis.

Authors:  Matías S Mora; Fernando J Mapelli; Oscar E Gaggiotti; Marcelo J Kittlein; Enrique P Lessa
Journal:  BMC Genet       Date:  2010-01-28       Impact factor: 2.797

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