Polygamy and purifying selection in birds.

Good genes theories of sexual selection predict that polygamy will be associated with more efficient removal of deleterious alleles (purifying selection), due to the alignment of sexual selection with natural selection. On the other hand, runaway selection theories expect no such alignment of natural and sexual selection, and may instead predict less efficient purifying selection in polygamous species due to higher reproductive variance. In an analysis of polymorphism data extracted from 150-bird genome assemblies, we show that polygamous species carry significantly fewer nonsynonymous polymorphisms, relative to synonymous polymorphisms, than monogamous bird species (p = .0005). We also show that this effect is independent of effective population size, consistent with the alignment of natural selection with sexual selection and "good genes" theories of sexual selection. Further analyses found no impact of polygamy on genetic diversity, while polygamy in females (polyandry) had a marginally significant impact (p = .045). We also recapitulate previous findings that smaller body mass and greater geographic range size are associated with more efficient purifying selection, more intense GC-biased gene conversion, and greater genetic diversity.

Next-generation metrics for monitoring genetic erosion within populations of conservation concern.

Genetic erosion is a major threat to biodiversity because it can reduce fitness and ultimately contribute to the extinction of populations. Here, we explore the use of quantitative metrics to detect and monitor genetic erosion. Monitoring systems should not only characterize the mechanisms and drivers of genetic erosion (inbreeding, genetic drift, demographic instability, population fragmentation, introgressive hybridization, selection) but also its consequences (inbreeding and outbreeding depression, emergence of large-effect detrimental alleles, maladaptation and loss of adaptability). Technological advances in genomics now allow the production of data the can be measured by new metrics with improved precision, increased efficiency and the potential to discriminate between neutral diversity (shaped mainly by population size and gene flow) and functional/adaptive diversity (shaped mainly by selection), allowing the assessment of management-relevant genetic markers. The requirements of such studies in terms of sample size and marker density largely depend on the kind of population monitored, the questions to be answered and the metrics employed. We discuss prospects for the integration of this new information and metrics into conservation monitoring programmes.

Genetic and genomic monitoring with minimally invasive sampling methods.

The decreasing cost and increasing scope and power of emerging genomic technologies are reshaping the field of molecular ecology. However, many modern genomic approaches (e.g., RAD-seq) require large amounts of high-quality template DNA. This poses a problem for an active branch of conservation biology: genetic monitoring using minimally invasive sampling (MIS) methods. Without handling or even observing an animal, MIS methods (e.g., collection of hair, skin, faeces) can provide genetic information on individuals or populations. Such samples typically yield low-quality and/or quantities of DNA, restricting the type of molecular methods that can be used. Despite this limitation, genetic monitoring using MIS is an effective tool for estimating population demographic parameters and monitoring genetic diversity in natural populations. Genetic monitoring is likely to become more important in the future as many natural populations are undergoing anthropogenically driven declines, which are unlikely to abate without intensive adaptive management efforts that often include MIS approaches. Here, we profile the expanding suite of genomic methods and platforms compatible with producing genotypes from MIS, considering factors such as development costs and error rates. We evaluate how powerful new approaches will enhance our ability to investigate questions typically answered using genetic monitoring, such as estimating abundance, genetic structure and relatedness. As the field is in a period of unusually rapid transition, we also highlight the importance of legacy data sets and recommend how to address the challenges of moving between traditional and next-generation genetic monitoring platforms. Finally, we consider how genetic monitoring could move beyond genotypes in the future. For example, assessing microbiomes or epigenetic markers could provide a greater understanding of the relationship between individuals and their environment.