Recent theoretical work in quantitative genetics has fueled interest in measuring natural selection in the wild. We discuss statistical and biological issues that may arise in applications of Lande and Arnold's (1983) multiple-regression approach to measuring selection. We review assumptions involved in estimation and hypothesis testing in regression problems, and we note difficulties that frequently arise as a result of violation of these assumptions. In particular, multicollinearity (extreme intercorrelation of characters) and extrinsic, unmeasured factors affecting fitness may seriously complicate inference regarding selection. Further, violation of the assumption that residuals are normally distributed vitiates tests of significance. For this situation, we suggest applications of recently developed jackknife tests of significance. While fitness regression permits direct assessment of selection in a form suitable for predicting selection response, we suggest that the aim of inferring causal relationships about the effects of phenotypic characters on fitness is greatly facilitated by manipulative experiments. Finally, we discuss alternative definitions of stabilizing and disruptive selection.

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Bacteria living on and in leaves and roots influence many aspects of plant health, so the extent of a plant's genetic control over its microbiota is of great interest to crop breeders and evolutionary biologists. Laboratory-based studies, because they poorly simulate true environmental heterogeneity, may misestimate or totally miss the influence of certain host genes on the microbiome. Here we report a large-scale field experiment to disentangle the effects of genotype, environment, age and year of harvest on bacterial communities associated with leaves and roots of Boechera stricta (Brassicaceae), a perennial wild mustard. Host genetic control of the microbiome is evident in leaves but not roots, and varies substantially among sites. Microbiome composition also shifts as plants age. Furthermore, a large proportion of leaf bacterial groups are shared with roots, suggesting inoculation from soil. Our results demonstrate how genotype-by-environment interactions contribute to the complexity of microbiome assembly in natural environments.

Glucosinolates are biologically active secondary metabolites of the Brassicaceae and related plant families that influence plant/insect interactions. Specific glucosinolates can act as feeding deterrents or stimulants, depending upon the insect species. Hence, natural selection might favor the presence of diverse glucosinolate profiles within a given species. We determined quantitative and qualitative variation in glucosinolates in the leaves and seeds of 39 Arabidopsis ecotypes. We identified 34 different glucosinolates, of which the majority are chain-elongated compounds derived from methionine. Polymorphism at only five loci was sufficient to generate 14 qualitatitvely different leaf glucosinolate profiles. Thus, there appears to be a modular genetic system regulating glucosinolate profiles in Arabidopsis. This system allows the rapid generation of new glucosinolate combinations in response to changing herbivory or other selective pressures. In addition to the qualitative variation in glucosinolate profiles, we found a nearly 20-fold difference in the quantity of total aliphatic glucosinolates and were able to identify a single locus that controls nearly three-quarters of this variation.