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Scale dependent inference in landscape geneticsAuthor(s): Samuel A. Cushman; Erin L. Landguth
Source: Landscape Ecology. 25: 967-979.
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
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DescriptionEcological relationships between patterns and processes are highly scale dependent. This paper reports the first formal exploration of how changing scale of research away from the scale of the processes governing gene flow affects the results of landscape genetic analysis. We used an individual-based, spatially explicit simulation model to generate patterns of genetic similarity among organisms across a complex landscape that would result given a stipulated landscape resistance model. We then evaluated how changes to the grain, extent, and thematic resolution of that landscape model affect the nature and strength of observed landscape genetic patternprocess relationships. We evaluated three attributes of scale including thematic resolution, pixel size, and focal window size. We observed large effects of changing thematic resolution of analysis from the stipulated continuously scaled resistance process to a number of categorical reclassifications. Grain and window size have smaller but statistically significant effects on landscape genetic analyses. Importantly, power in landscape genetics increases as grain of analysis becomes finer. The analysis failed to identify the operative grain governing the process, with the general pattern of stronger apparent relationship with finer grain, even at grains finer than the governing process. The results suggest that correct specification of the thematic resolution of landscape resistance models dominates effects of grain and extent. This emphasizes the importance of evaluating a range of biologically realistic resistance hypotheses in studies to associate landscape patterns to gene flow processes.
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CitationCushman, Samuel A.; Landguth, Erin L. 2010. Scale dependent inference in landscape genetics. Landscape Ecology. 25: 967-979.
Keywordslandscape genetics, scale, grain, extent, thematic resolution, gradient, pattern, process, gene flow, simulation
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