Scale dependent inference in landscape genetics |
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Authors: | Samuel A Cushman Erin L Landguth |
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Institution: | (1) US Forest Service, Rocky Mountain Research Station, 800 E Beckwith, Missoula, MT 59801, USA;(2) Individualized Interdisciplinary Graduate Program, Mathematics Building, University of Montana, 800 E Beckwith, Missoula, MT 59801, USA |
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Abstract: | Ecological 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 pattern–process 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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