= Solution
There is no unconditional prediction that a difficult environment must increase both rates. <Mutation> produces new <alleles>, whereas recombination rearranges existing <alleles>; each can improve future variation while imposing an immediate cost. Predictions must distinguish a reversible physiological response from evolution of an inherited rate modifier, and predictable environmental cues from genuinely uninformative fluctuations.
In a well-adapted population, most new <mutations> are neutral or deleterious, so increased <mutation> can raise <mutation load>. During adaptation, a mutator can nevertheless become associated with a beneficial <mutation> that it generated and spread by <genetic hitchhiking>. This association is strongest in clonal populations and can be weakened by recombination. After adaptation, deleterious effects can favour lower rates again, although changing rate requires available modifiers and depends on drift and selection. A mechanism that raises <mutation> only in a particular stressed state can have a different long-term cost from a permanently high genomic rate.
Empirical support is conditional. <Paul D. Sniegowski> and colleagues observed spontaneous mutator evolution during long-term adaptation of laboratory <bacteria>. This demonstrates that rate evolution can occur; it does not establish that mutators always win in stressful conditions or that each <mutation> is directed toward the needed <phenotype>.<Evolution of high mutation rates in experimental populations of E. coli by Paul D. Sniegowski and others>[Emergence of mutator lineages during adaptation]{ref} Increased recovery of selected variants is not itself a measured mutation-rate increase: differential growth, survival and pre-existing variants can change their frequency. Mutation-accumulation measurements, neutral reporters and appropriately interpreted fluctuation analyses address different parts of that problem.
Recombination can assemble favourable <alleles> from different backgrounds and reduce <Hill-Robertson interference>. Under changing selection it can generate combinations not already common, but it can also break well-adapted combinations. In a simple two-locus model, recombination changes the frequency of the $AB$ haplotype by $\Delta x_{AB}=-rD$, where $D=x_{AB}x_{ab}-x_{Ab}x_{aB}$. If favourable <alleles> are concentrated in competing $Ab$ and $aB$ backgrounds, then $D<0$ and recombination produces more $AB$; if $D>0$, the immediate effect reverses. Thus an argument about recombination must specify the associations that selection and environment generate.
The <Red Queen hypothesis> gives a specific changing environment: coevolving parasites can repeatedly shift which host <genotypes> perform well. <Levi T. Morran> and colleagues found increased outcrossing under experimental host-parasite coevolution in nematodes. That supports a benefit of biparental reproduction in that system, but outcrossing is not identical to crossover frequency within meiosis, and the result is not a universal prediction for every abiotic challenge.<Running with the Red Queen: host-parasite coevolution selects for biparental sex by Levi T. Morran and others>[Experimental changes in outcrossing under coevolution]{ref}
Environmental predictability determines whether a cue-sensitive response is useful. A cue that forecasts a sustained new condition can favour one regulatory strategy; rapid, uncorrelated fluctuations can favour robustness, plasticity or diversified offspring instead. Long-term growth depends on compounded reproductive success, often represented by expected log fitness, rather than the arithmetic benefit in one favourable episode. Tests should therefore compare replicated populations and time courses, measure rate modifiers directly, and distinguish sexual reproduction, outcrossing, <gene> conversion and crossing over. \b[The evidence supports several mechanisms for conditional increases, not a general law that adversity increases evolvability.]
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