Summations Only

Definitions are context dependent. Macroevolution is the accumulation of what you call trivial changes.

It would be extremely helpful if you would use scientific statements instead of hyperbolic ones.

Defining what a holobaramin is does not tell us how you determine which species belong in an holobaramin. You don't list any criteria for determining which species belong to the same kind.

False. The emergence of antibiotic resistance also includes random mutations and/or horizontal genetic transfer followed by natural selection. The combination of genetic changes and natural selection adds up to evolution. First, natural selection nor common descent are evolution.Secondly, are you saying that observations of evolution in action is not evidence for evolution?Edited by Taq, : No reason given.

That, my friend, is what we call projection. You go to creationist websites that publish known lies. You repeat them. When those lies are exposed, you try to diminish this problem by calling everyone else liars.When you are ready to stop repeating creationist lies published on creationist websites, let us know.

I already demonstrated that evolution is useful with many different examples. Seems your memory is a bit dodgy.

You can start an experiment with a single bacterium and grow an entire population from that single founder. What you will find is that 1 in a few hundred million bacteria will produce resistance to different kinds of antibiotic. This isn't a case of pre-existing variation. This is a case of mutations producing new characteristics.The same applies for the pocket mice. We know which mutations produce black fur, and we also know that those mutations had to arise in a population of brown mice because the black rocks they are found on are very recent (geologically speaking). Common descent is a conclusion, not a theory.

My preferred definition for species is "a population that is evolving together".

That's where statistics comes in. If there is a statistically significant difference in allele distributions between two defined populations then you can objectively define "evolving separately".

As shown by the experiment, those variations are produced by mutations.

That's why natural selection by itself is not the totality of evolution. You also need random mutations (with respect to fitness) producing new variations.

Universal common descent is useful for explaining the distribution of characteristics amongst all life, which is a very useful theory in biology.

If you randomly pick a card out of a deck you will pick the same card with enough draws. Those are still random draws even though a pick is repeated. I said they are random (with respect to fitness), not novel. It is entirely possible for two populations to produce the same random mutation in the same way that you can pick the same card from two random draws. That process includes the mutations that produce the two colors. Claims are not necessarily a theory. A claim can also be a conclusion drawn from evidence, which is the case with universal common descent. Conclusions and theories are two different things.Edited by Taq, : No reason given.Edited by Taq, : No reason given.

Since the entire purpose of science is to explain observations, it kind of goes without saying. Are you saying that this explanation is not true because it is not "useful" in your estimation? That is false. Universal common descent is a conclusion, not a theory.It is also of practical use, such as the SIFTER algorithm that can predict protein function:

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PLoS Comput Biol. 2005 Oct;1(5):e45. Epub 2005 Oct 7.

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Protein molecular function prediction by Bayesian phylogenomics.

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Engelhardt BE1, Jordan MI, Muratore KE, Brenner SE.

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We present a statistical graphical model to infer specific molecular function for unannotated protein sequences using homology. Based on phylogenomic principles, SIFTER (Statistical Inference of Function Through Evolutionary Relationships) accurately predicts molecular function for members of a protein family given a reconciled phylogeny and available function annotations, even when the data are sparse or noisy. Our method produced specific and consistent molecular function predictions across 100 Pfam families in comparison to the Gene Ontology annotation database, BLAST, GOtcha, and Orthostrapper. We performed a more detailed exploration of functional predictions on the adenosine-5‘-monophosphate/adenosine deaminase family and the lactate/malate dehydrogenase family, in the former case comparing the predictions against a gold standard set of published functional characterizations. Given function annotations for 3% of the proteins in the deaminase family, SIFTER achieves 96% accuracy in predicting molecular function for experimentally characterized proteins as reported in the literature. The accuracy of SIFTER on this dataset is a significant improvement over other currently available methods such as BLAST (75%), GeneQuiz (64%), GOtcha (89%), and Orthostrapper (11%). We also experimentally characterized the adenosine deaminase from Plasmodium falciparum, confirming SIFTER's prediction. The results illustrate the predictive power of exploiting a statistical model of function evolution in phylogenomic problems. A software implementation of SIFTER is available from the authors.

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Yes, it did. The new variation was the black color caused by mutations.