Not the origin of genome complexity

Over the past decade evolutionary geneticist Mike Lynch has been articulating a model of genome complexity which relies on stochastic factors as the primary motive force by which genome size increases. The argument is articulated in a 2003 paper, and further elaborated in his book The Origins of Genome Architecture. There are several moving parts in the thesis, some of which require a rather fine-grained understanding of the biophysical structural complexity of the genome, the nature of Mendelian inheritance as a process, and finally, population genetics. But the core of the model is simple: there is an inverse relationship between long term effective population size and genome complexity. Low individual numbers ~ large values in terms of base pairs and counts of genetic elements such as introns. A quick reminder: effective population size denotes the proportion of the population which contributes genes to the next generation. So, in the case of insects with extremely high mortality in the larval stage the effective population size may be orders of magnitude smaller than the census size at any given generation evaluating over all stages of life history. In contrast, with humans a much larger proportion of children end up contributing to the genetic makeup of the subsequent generation. With large organisms I've heard you can sometimes use a rule of thumb that effective population size is ~1/3 of census size, though this probably overestimates the effective population size. One reason that reproductive variation reduces the effective population, because many individuals contribute far less to the next generation than other individuals. The greater the variance, the more evolutionary genetic variation is impacted by a few individuals within the population at a given generation, reducing effective population which contributes to the next (the reproductive variance is often assumed to be poisson, but that is likely an underestimate). Additionally, there is the issue of variation over time. Long term effective population is much more sensitive to low bound values than high bound values, so it is liable to be much smaller than the census size at any given period for a species which goes through cycles. Humans for example have a relatively small long term effective population size evaluated over the past 100,000 years because we seem to have expanded from a small initial population. Mathematically since long term effective population size is given by the harmonic mean it stands to reason that low bound values would be critical. If that doesn't make sense to you, remember the outsized impact which population bottlenecks may have on the long term trajectory of a species, in particular by removing genetic variation. How does this influence genome complexity? Basically Lynch's thesis is that when you reduce effective population you dampen the power of natural selection, specifically purifying selection, from preventing the addition of non-adaptive complexity through random processes. It isn't that selection is rendered moot, rather, its signal is overwhelmed by the noise. Here's the abstract of his 2003 paper: