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For lakes that were sampled in multiple years, we pooled sampling years because visual inspection of the data showed that there were no major differences among years and pooling improved sample size.
However, while the use of many genes does reduce stochastic errors (due to improved sample size), it simultaneously makes systematic errors more apparent [ 3].
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This chapter presents Bayesian methods to improve sample size approximations.
We show how the results from stage I can improve sample size calculations for stage II adaptively.
To improve sample sizes, we therefore also present the same LMM, excluding the sex of the offspring.
Stability improved with sample size and tended towards an asymptote at more than 60 locations for MCP and KDE.
Hit rates improved as sample size, the magnitude of the magnitude of the position effect, and the size of position change between two forms increased.
Many of the locations sampled in our dataset were newly sequenced for this study (n = 33), for some locations from Shikano et al. [13] we improved the sample size (n = 21), and for some locations we only used the previously published data (n = 3).
We propose the following characteristics to improve sample design: (1) increase sample size, (2) prefer systematic to random sampling and (3) include environmental information in the design.
Coverage probabilities improve as sample size increases.
These biases do not improve as sample size increased.
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