Phylogeographic inferences about gene flow are strengthened through comparison of co-distributed taxa, but also depend on adequate genomic sampling. Amplified fragment length polymorphisms (AFLPs) provide a rapid and inexpensive source of multilocus allele frequency data for making genomically robust inferences. Every AFLP study initially generates markers with a range of locus-specific genotyping error rates and applies criteria to select a subset for analysis. However, there has been very little empirical evaluation of the best tradeoff between culling all but the lowest-error loci to minimize overall genotyping error versus the potential for increasing population genetic signal by retaining more loci. Here, we used AFLPs to compare population structure in co-distributed broadcast spawning (Crassostrea virginica) and brooding (Ostrea equestris) oyster species. Using existing methods for almost entirely automated marker selection and scoring, genotyping error tradeoffs were evaluated by comparing results across a nested series of data sets with mean mismatch errors of 0, 1, 2, 3, 4 and 44%. Artifactual population structure was diagnosed in high-error data sets and we assessed the low-error point at which expected population substructure signal was lost. In both species, we identified substructure patterns deemed to be inaccurate at average mismatch error rates p2 and 44%. In the species comparison, the optimum data sets showed higher gene flow for the brooding oyster with more oceanic salinity tolerances. AFLP tradeoffs may differ among studies, but our results suggest that important signal may be lost in the pursuit of 'acceptable' error levels and our procedures provide a general method for empirically exploring these tradeoffs.

Temperature distributions from heating in a domestic microwave oven were studied by considering the coupling between the electromagnetics and heat transfer through changes in dielectric properties during heating. Maxwell's equations for electromagnetics and the thermal energy equations are solved numerically using two separate finite-element softwares. The coupling between the softwares was developed by writing special modules that interfaced these softwares at the system level. Experimentally measured temperature profiles were compared with the numerical predictions. The importance of coupling was evident when the properties changed significantly with temperature for low and high dielectric loss materials and more so for the high loss materials. For moderate loss materials, when the properties do not change as much with temperature, coupled solutions lead to results very close to the results for the uncoupled solution.

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