No abstract

Crop populations derived from experimental crosses enable the genetic dissection of complex traits and support modern plant breeding. Among these, multi-parent populations now play a central role. By mixing and recombining the genomes of multiple founders, multi-parent populations combine many commonly sought beneficial properties of genetic mapping populations. For example, they have high power and resolution for mapping quantitative trait loci, high genetic diversity and minimal population structure. Many multi-parent populations have been constructed in crop species, and their inbred germplasm and associated phenotypic and genotypic data serve as enduring resources. Their utility has grown from being a tool for mapping quantitative trait loci to a means of providing germplasm for breeding programmes. Genomics approaches, including de novo genome assemblies and gene annotations for the population founders, have allowed the imputation of rich sequence information into the descendent population, expanding the breadth of research and breeding applications of multi-parent populations. Here, we report recent successes from crop multi-parent populations in crops. We also propose an ideal genotypic, phenotypic and germplasm 'package' that multi-parent populations should feature to optimise their use as powerful community resources for crop research, development and breeding. Over recent years, numerous multi-parent populations (MPPs) have been successfully developed in crops (Huang et al. 2015; Cockram and Mackay 2018). MPPs bring together key genomic, phenotypic and germplasm resources to form a

Competition over reproductive opportunities among members of one sex often harms the opposite sex, creating a conflict of interest between individual males and females. Recently, this battle of the sexes has become a paradigm in the study of intersexual coevolution. Here, we review recent theoretical and empirical advances suggesting that -as in any scenario of intraspecific competition -selfishness (competitiveness) can be influenced by the genetic relatedness of competitors. When competitors are positively related (e.g. siblings), an individual may refrain from harming its competitor(s) and their mate(s) because this can improve the focal individual's inclusive fitness. These findings reveal that population genetic structure might be of paramount importance when studying the battle of the sexes. We conclude by identifying some new lines of research at the interface of sexual selection and social evolution.

Bayesian foraging in patchy environments requires that foragers have information about the distribution of resources among patches (prior information), either set by natural selection or learned from past experience. We test the hypothesis that bumblebee foragers can rapidly learn prior information from past experience in two very different experimental environments. In the high-variance environment (patches of low and high quality), stochastic optimality models predicted that finding rewards should sometimes sharply increase an optimal forager's tendency to stay in a patch (an incremental response), whereas in the uniform environment, finding rewards should always decrease the tendency to stay (a decremental response). We use Cox regression models to show that, in a matter of hours, bees learned to match both predicted responses, resulting in a reward intake rate that averaged 80% of the predicted maximum. Following training in either environment, bees' adaptive behavior carried over to a common test environment, thus confirming the influence of memorized prior information. Although Bayesian foraging by learning is often presumed, this study is the first to clearly isolate the adaptive use of a learned prior expectation. More generally, it highlights the remarkable adaptive plasticity of an important generalist pollinator and agent of selection.

Animals often convey useful information, despite a conflict of interest between the signaller and receiver. There are two major explanations for such 'honest' signalling, particularly when the size or intensity of signals reliably indicates the underlying quality of the signaller. Costly signalling theory (including the handicap principle) predicts that dishonest signals are too costly to fake, whereas the index hypothesis predicts that dishonest signals cannot be faked. Recent evidence of a highly conserved causal link between individual quality and signal growth appears to bolster the index hypothesis. However, it is not clear that this also diminishes costly signalling theory, as is often suggested. Here, by incorporating a mechanism of signal growth into costly signalling theory, we show that index signals can actually be favoured owing to the cost of dishonesty. We conclude that costly signalling theory provides the ultimate, adaptive rationale for honest signalling, whereas the index hypothesis describes one proximate (and potentially very general) mechanism for achieving honesty.