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Mol Ecol. 2010 Oct;19(19):4179-91. doi: 10.1111/j.1365-294X.2010.04808.x. Epub 2010 Sep 6.

Quantifying the lag time to detect barriers in landscape genetics.

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1
University of Montana, Mathematics Building, Missoula, MT, 59812, USAUSDA Forest Service, Rocky Mountain Research Station, 800 E Beckwith Ave., Missoula, MT 59801, USAColorado State University, Biology Department, Fort Collins, CO 80523-1878 USAFlathead Lake Biological Station, Division of Biological Sciences, University of Montana, Polson, MT 59860, USACentro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto (CIBIO-UP), Campus Agrário de Vairão, 4485-661 Vairão, Portugal.

Abstract

Understanding how spatial genetic patterns respond to landscape change is crucial for advancing the emerging field of landscape genetics. We quantified the number of generations for new landscape barrier signatures to become detectable and for old signatures to disappear after barrier removal. We used spatially explicit, individual-based simulations to examine the ability of an individual-based statistic [Mantel's r using the proportion of shared alleles' statistic (Dps)] and population-based statistic (FST ) to detect barriers. We simulated a range of movement strategies including nearest neighbour dispersal, long-distance dispersal and panmixia. The lag time for the signal of a new barrier to become established is short using Mantel's r (1-15 generations). FST required approximately 200 generations to reach 50% of its equilibrium maximum, although G'ST performed much like Mantel's r. In strong contrast, FST and Mantel's r perform similarly following the removal of a barrier formerly dividing a population. Also, given neighbour mating and very short-distance dispersal strategies, historical discontinuities from more than 100 generations ago might still be detectable with either method. This suggests that historical events and landscapes could have long-term effects that confound inferences about the impacts of current landscape features on gene flow for species with very little long-distance dispersal. Nonetheless, populations of organisms with relatively large dispersal distances will lose the signal of a former barrier within less than 15 generations, suggesting that individual-based landscape genetic approaches can improve our ability to measure effects of existing landscape features on genetic structure and connectivity.

KEYWORDS:

computer simulation; connectivity; conservation genetics; gene flow; habitat fragmentation; landscape modelling; power analysis; resistance surfaces; spatial analysis

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