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This question is in turn just a special case of the general problem of historical inference.
As description, this is fair enough, but any historical inference reached or evolutionary trend suggested, certainly one so detailed and complex as theirs, begs for broader contextualization and explanation.
One fundamental problem with methods of historical inference is that their performances cannot be easily evaluated.
Natural populations of known detailed past demographic history are extremely valuable to evaluate methods of historical inference, yet are extremely rare.
We also suggest that controlling demographic history in laboratory-cultured populations could be extended to test and validate historical inference methods under a wide range of realistic parameters.
Thus, while simulation studies are essential to identify conditions under which a method can fail to properly estimate a given parameter, we may not be able to entirely rely upon them to fully validate a method of historical inference.
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Historical inferences of effective population size (Ne) for Zosterops taxa were inferred using the pairwise sequentially Markovian coalescent model (PSMC; Li and Durbin 2011).
Locally applied versions of an evolution education curriculum that follow this line of thinking would accordingly seek as a goal that students discover that evidence for evolutionary history is in organisms all around them, and make historical inferences from available observations.
Archeologists and other anthropologists who apply evolutionary theory to material things have developed methods to detect and measure, and thereby control for, horizontal transmission (Currie et al. 2010) or at least to identify the conditions that make historical inferences robust to its effects (Nunn et al. 2010).
We think a strength of this model is its direct depiction of evolutionary events allowing historical inferences rather than phenetic approaches (such as split-graphs representations or clustering genomes by genome content etc ).
● The need to implement proactive job monitoring services that apply historical pattern inference to determine status of jobs.
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