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Genome size varies widely among different organisms, and is not very closely correlated with complexity of the organism. In species with large genomes, most of the DNA does not code for genes. This chapter explores the “selfish DNA” hypothesis for genome size. It also discusses vertebrate cases of small genome size (e.g., pufferfish) and extraordinarily large genome size (e.g., species of salamanders). A consequence of the huge genome in these salamanders is that their brains are less complex. Michael Lynch has proposed that much of the variation in genome size may be explained by variation in effective population size. The chapter concludes with a discussion of how genome size may be related to extinction risk and hence, conservation biology.

This chapter examines with four unlinked loci the extent of divergence between two linguistically related Baining groups in New Britain. Although they are linguistically related and are less than 100 km apart, they are, by a number of measures, surprisingly different genetically. This difference is explained in terms of male and female demographic distinctions. Early comparisons in global and regional mtDNA and NRY diversity indicate comparatively greater overall mtDNA variability, but greater among-group NRY variation. The chapter suggests that the key factor is the larger effective population size of women (since relatively few men contribute to following generations). This distinction could cause an acceleration in the effects of genetic drift, leading to less overall variation, but proportionately more among-group variation. In the Baining study, evidence is found for a much smaller male effective population size. However, the proportion of males who migrate and successfully reproduce appears to be greater than for females. In considering the surprising degree of overall differentiation between these two Baining groups, the effects of drift are paramount, but there remains the question of whether the differences may be due to the residue of ancient lineage sorting.

We expect heterozygosity to be lost at a rate of 1/2N per generation in an ideal population because of genetic drift where N is the census population size. The effective size of a population is the size of the ideal (Wright–Fisher) population that will result in the same amount of genetic drift as in the actual population being considered. Heterozygosity is generally lost at a rate much faster than 1/2N in natural populations primarily because reproductive success is much more variable than assumed in an ideal population. Therefore, the effective size of natural populations (N_e) is often much smaller than the census population size (N_e << N). Predicting the rate of loss of heterozygosity over calendar time in a population requires an estimate of both N_e and the generation interval. Genomic techniques provide a variety of methods to estimate N_e in natural populations.

The effects of genetic drift usually assume an idealized population of constant size. This chapter shows how the population size for such an idealized population can be replaced with an effective population size for populations with age structure, unequal sex ratios, a history of expansion or contraction, inbreeding, and population subdivision. These demographic features impact the entire genome more or less equally. A relatively recent understanding is that selection at a site can dramatically reduce the local effective population size experienced by nearby linked sites (the Hill-Robertson effect). This can arise from background selection to remove deleterious new mutations or from selective sweeps wherein favorable new mutations are driven toward fixation. The Hill-Robertson effect is a general way to describe the fact that selection at a site makes selection are other linked sites less efficient, and, therefore, more neutral. This chapter discusses the implications of this finding for genome structure.

Late life was first detected in human populations, despite the very late occurrence of late life in humans. Recent data from supercentenarians provide strong evidence of a late-life mortality rate plateau in human populations. An important puzzle is why human populations reach late life so late. Two explanations are conceivable, and not necessarily incompatible with each other: (i) a generally increased mortality level under evolutionarily novel conditions due to a lack of time for age-independent beneficial substitutions to increase in frequency; (ii) a recent expansion in effective population sizes, greatly prolonging the age-range over which the effective force of natural selection declines. Regardless of its evolutionary explanation, the cessation of aging in human populations suggests new possibilities for the extension of human healthspan.

In a finite population, drift is often more important than selection in removing any initial additive variance. This chapter examines the joint impact of selection, drift, and mutation on the long-term response in a quantitative trait. One key result is the remarkable finding of Robertson that the expected long-term response from any initial additive variance is bounded above by the product of twice the effective population size times the initial response. This result implies that the optimal selection intensity for long-term response it to save half of the population in each generation.

Genetic management of fragmented populations involves the application of evolutionary genetic theory and knowledge to alleviate problems due to inbreeding and loss of genetic diversity in small population fragments. Populations evolve through the effects of mutation, natural selection, chance (genetic drift) and gene flow (migration). Large outbreeding, sexually reproducing populations typically contain substantial genetic diversity, while small populations typically contain reduced levels. Genetic impacts of small population size on inbreeding, loss of genetic diversity and population differentiation are determined by the genetically effective population size, which is usually much smaller than the number of individuals.

Chapter 3, “Quantifying genetic variation at the molecular level,” introduces quantitative methods for measuring variation directly in DNA sequences to help decipher fundamental properties of populations and what they can tell us about evolution. It provides an overview of the evolutionary factors that contribute to genetic variation, like mutational input, effective population size, genetic drift, migration rate, and models of migration. This chapter surveys the principal ways to measure and summarize polymorphisms within a single population and across multiple populations of a species, including heterozygosity, nucleotide polymorphism estimators of θ‎, the site frequency spectrum, and F_ST, and by providing illustrative natural examples. Populations are where evolution starts, after mutations arise as the spark of population genetic variation, and Chapter 3 describes how to quantify the variation to connect observations to predictions about how much polymorphism there ought to be under different circumstances.

Genetic management of fragmented populations involves the application of evolutionary genetic theory and knowledge to alleviate problems due to inbreeding and loss of genetic diversity in small population fragments. Populations evolve through the effects of mutation, natural selection, chance (genetic drift), and gene flow. Large outbreeding sexually reproducing populations typically contain substantial genetic diversity, while small populations typically contain reduced levels. Genetic impacts of small population size on inbreeding, loss of genetic diversity and population differentiation are determined by the genetically effective population size, which is usually much smaller than the number of individuals.

Genetics plays an increasing role in monitoring demographic and genetic changes in populations over time. One of the most powerful advances in genetic monitoring is the development of techniques to detect trace amounts of DNA in noninvasive samples (e.g., feathers, skin, etc.) and environmental DNA (eDNA) from elusive and rare species in water and soil samples. Individual genotypes from noninvasive samples such as feces and hair can be used to estimate abundance, survival, and other demographic parameters using mark–recapture analysis. Genetic monitoring of heterozygosity, allelic diversity, and effective population size allows managers to detect genetic changes in response to environmental perturbations or management actions. Genomic methods now allow detection and monitoring of adaptive alleles; for example, to test whether these alleles increase in frequency in response to environmental change, demonstrating an adaptive response, stress, or a die-off (e.g., caused by infectious disease pathogens).

Evolution is the change in heritable traits of populations over successive generations. At the molecular level this translates into changes in their genetic composition. A general theoretical investigation of how different demographic and evolutionary processes affect genetic variation within and between populations provides us with tools to reconstruct evolutionary history. This is the fundamental purpose of population genetics. This chapter investigates the relationship between allele and genotype frequencies in a hypothetical population that is not subjected to any evolutionary forces—i.e. the Hardy–Weinberg model. Then, one by one, demographic and evolutionary factors such as non-random mating, genetic drift, selection, mutation, and gene flow are introduced to investigate in what ways they affect allele and/or genotype frequencies. The chapter further introduces F-statistics and goodness of fit tests to investigate statistical deviations from expectations.

Conservation biology as a discipline focused on endangered species is young and dates only from the late 1970s. Although conservation of endangered species encompasses many different biological disciplines, including behavior, ecology, and genetics, evolutionary considerations always have been emphasized (e.g., Frankel and Soule 1981). Many of the applications of evolutionary concepts to conservation are ones related to genetic variation in small or subdivided populations. However, the critical status of many endangered species makes both more precision and more caution necessary than the general findings for evolutionary considerations. On the other hand, the dire situations of many endangered species often require recommendations to be made on less than adequate data. Overall, one can think of the evolutionary aspects of conservation biology as an applied aspect of the evolution of small populations with the important constraint that any conclusions or recommendations may influence the actual extinction of the populations or species under consideration. From this perspective, all of the factors that influence continuing evolution (i.e., selection, inbreeding, genetic drift, gene flow, and mutation; e.g., Hedrick 2000) are potentially important in conservation. The evolutionary issues of widest concern in conservation biology’”inbreeding depression and maintenance of genetic variation’” can be seen in their simplest form as the joint effects of inbreeding and selection, and of genetic drift and mutation, respectively. However, even in model organisms such as Drosophila, the basis of inbreeding depression and the maintenance of genetic variation are not clearly understood. In addition, findings from model laboratory organisms may not provide good insight into problems in many endangered species, the most visible of which are generally slowly reproducing, large vertebrates with small populations. Here we will first focus on introductions to two important evolutionary aspects of conservation biology: the units of conservation and inbreeding depression. Then, we will discuss studies in two organisms as illustrations of these and related principles’”an endangered fish species, the Gila topminnow, and desert bighorn sheep’”to illustrate some evolutionary aspects of conservation. In the discussion, we will mention some of the other evolutionary topics that are relevant to conservation biology.