Comparison of Mandibular Phenotypic and Genetic Integration between Baboon and Mouse

In this study we compare patterns of mandibular integration between mice and baboons using both phenotypic and quantitative genetic data. Specifically, we test how well each species fits with the mosaic model of mandibular integration suggested by Atchley and Hall (Biol Rev Camb Philos Soc 66:101–157, 1991) based on developmental modules. We hypothesize that patterns of integration will be similar for mice and baboons and that both species will show strong integration within developmental modules and weaker integration between modules. Corresponding landmark data were collected from the hemi-mandibles of an advanced intercross mouse sample (N = 1239) and mandibles from a baboon sample of known pedigree from the Southwest Foundation for Biomedical Research (N = 430). We used four methods of analysis to quantify and compare the degree of mandibular integration between species including two methods based on a priori assumptions, and two a posteriori analyses. We found that patterns of integration are broadly similar for baboon and mouse mandibles, with both species displaying a modular pattern of integration. While there is a general trend of similarity in integration patterns between species, there were some marked differences. Mice are strongly correlated among distances within the coronoid process and the incisive alveolar region, whereas baboons are strongly integrated within the condylar process. We discuss the potential evolutionary implications of the similar patterns of integration between these species with an emphasis on the role of modularity. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Analysis of Misclassified Correlated Binary Data Using a Multivariate Probit Model when Covariates are Subject to Measurement Error

A multivariate probit model for correlated binary responses given the predictors of interest has been considered. Some of the responses are subject to classification errors and hence are not directly observable. Also measurements on some of the predictors are not available; instead the measurements on its surrogate are available. However, the conditional distribution of the unobservable predictors given the surrogate is completely specified. Models are proposed taking into account either or both of these sources of errors. Likelihood‐based methodologies are proposed to fit these models. To ascertain the effect of ignoring classification errors and /or measurement error on the estimates of the regression and correlation parameters, a sensitivity study is carried out through simulation. Finally, the proposed methodology is illustrated through an example.     

Evolution in stressful environments. I. Phenotypic variability, phenotypic selection, and response to selection in five distinct environmental stresses

Considerable debate has accompanied efforts to integrate the selective impacts of environmental stresses into models of life-history evolution. This study was designed to determine if different environmental stresses have consistent phenotypic effects on life-history characters and whether selection under different stresses leads to consistent evolutionary responses. We created lineages of a wild mustard (Sinapis arvensis) that were selected for three generations under five stress regimes (high boron, high salt, low light, low water, or low nutrients) or under near-optimal conditions (control). Full-sibling families from the six selection histories were divided among the same six experimental treatments. In that test generation, lifetime plant fecundity and six phenotypic traits were measured for each plant. Throughout this greenhouse study, plants were grown individually and stresses were applied from the early seedling stage through senescence. Although all stresses consistently reduced lifetime fecundity and most size- and growth-related traits, different stresses had contrasting effects on flowering time. On average, stress delayed flowering compared to favorable conditions, although plants experiencing low nutrient stress flowered earliest and those experiencing low light flowered latest. Contrary to expectations of Grime's triangle model of life-history evolution, this ruderal species does not respond phenotypically to poor environments by flowering earlier. Most stresses enhanced the evolutionary potential of the study population. Compared with near-optimal conditions, stresses tended to increase the opportunity for selection as well as phenotypic variance, although both of these quantities were reduced in some stresses. Rather than favoring traits characteristic of stress tolerance, such as slow growth and delayed reproduction, phenotypic selection favored stress-avoidance traits: earlier flowering in all five stress regimes and faster seedling height growth in three stresses. Phenotypic correlations reinforced direct selection on these traits under stress, leading to predicted phenotypic change under stress, but no significant selection in the control environment. As a result of these factors, selection under stress resulted in an evolutionary shift toward earlier flowering. Environmental stresses may drive populations of ruderal plant species like S. arvensis toward a stress-avoidance strategy, rather than toward stress tolerance. Further studies will be needed to determine when selection in stressful environments leads to these alternative life-history strategies.     

Using evolutionary tools to facilitate the prediction and prevention of host‐based differentiation in biological control: a review and perspective

The unprecedented success of biological control (biocontrol) agents led some of the proponents of this technology to promote its use as a panacea for all pest problems. Following an accumulation of non‐target host interactions, because of generalist or new association introductions, techniques to help ensure classical biocontrol agent's success and reduce non‐target interactions were implemented. Even with these new measures in place, public and scientific mistrust and lack of consistency has resulted in increased regulation of biocontrol introductions. This has likely decreased the probability of effective, sustainable control measures being expeditiously implemented. With the current apprehension concerning the safety of biocontrol, we should incorporate the processes (adaptation, selection, etc.) and theoretical concepts of evolutionary biology to predict and enhance the effectiveness of biocontrol. The microevolutionary perspective that involves mutation, drift, selection and gene flow may be a crucial consideration in the realm of biocontrol. Here, we discuss how and why spatial and evolutionary models should be implemented into future risk assessment analyses of potential biocontrol agents. We suggest that it is necessary to re‐assess the approach that has developed over the past approximately 100 years of sustained releases and illuminate them in the context of an evolutionary timescale.