Experimental Evidence of an Eco-evolutionary Feedback during Adaptive Divergence

Differences in how organisms modify their environment can evolve rapidly and might influence adaptive population divergence [1, 2]. In a common garden experiment in aquatic mesocosms, we found that adult stickleback from a recently diverged pair of lake and stream populations had contrasting effects on ecosystem metrics. These modifications were caused by both genetic and plastic differences between populations and were sometimes comparable in magnitude to those caused by the presence/ absence of stickleback. Lake and streamfish differentially affected the biomass of zooplankton and phytoplankton, the concentration of phosphorus, and the abundance of several prey (e.g., copepods) and non-prey (e.g., cyanobacteria) species. The adult mediated effects on mesocosm ecosystems influenced the survival and growth of a subsequent generation of juvenile stickleback reared in the same mesocosms. The prior presence of adults decreased the overall growth rate of juveniles, and the prior presence of stream adults lowered overall juvenile survival. Among the survivors, lake juveniles grew faster than co-occurring stream juveniles, except in mesocosm ecosystems previously modified by adult lake fish that were reared on plankton. Overall, our results provide evidence for reciprocal interactions between ecosystem dynamics and evolutionary change (i.e., eco-evolutionary feedbacks) in the early stages of adaptive population divergence.

More adaptive versus less maladaptive coping: What is more predictive of symptom severity? Development of a new scale to investigate coping profiles across different psychopathological syndromes

BACKGROUND: Lack of adaptive and enhanced maladaptive coping with stress and negative emotions are implicated in many psychopathological disorders. We describe the development of a new scale to investigate the relative contribution of different coping styles to psychopathology in a large population sample. We hypothesized that the magnitude of the supposed positive correlation between maladaptive coping and psychopathology would be stronger than the supposed negative correlation between adaptive coping and psychopathology. We also examined whether distinct coping style patterns emerge for different psychopathological syndromes. METHODS: A total of 2200 individuals from the general population participated in an online survey. The Patient Health Questionnaire-9 (PHQ-9), the Obsessive-Compulsive Inventory revised (OCI-R) and the Paranoia Checklist were administered along with a novel instrument called Maladaptive and Adaptive Coping Styles (MAX) questionnaire. Participants were reassessed six months later. RESULTS: MAX consists of three dimensions representing adaptive coping, maladaptive coping and avoidance. Across all psychopathological syndromes, similar response patterns emerged. Maladaptive coping was more strongly related to psychopathology than adaptive coping both cross-sectionally and longitudinally. The overall number of coping styles adopted by an individual predicted greater psychopathology. Mediation analysis suggests that a mild positive relationship between adaptive and certain maladaptive styles (emotional suppression) partially accounts for the attenuated relationship between adaptive coping and depressive symptoms. LIMITATIONS: Results should be replicated in a clinical population. CONCLUSIONS: Results suggest that maladaptive and adaptive coping styles are not reciprocal. Reducing maladaptive coping seems to be more important for outcome than enhancing adaptive coping. The study supports transdiagnostic approaches advocating that maladaptive coping is a common factor across different psychopathologies.

Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between “a priori” methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and “a posteriori” methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state-of-the-art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real-world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.

Denoising Your Monte Carlo Renders: Recent Advances in Image Space-Adaptive Sampling and Reconstruction

With the ongoing shift in the computer graphics industry toward Monte Carlo rendering, there is a need for effective, practical noise-reduction techniques that are applicable to a wide range of rendering effects and easily integrated into existing production pipelines. This course surveys recent advances in image-space adaptive sampling and reconstruction algorithms for noise reduction, which have proven very effective at reducing the computational cost of Monte Carlo techniques in practice. These approaches leverage advanced image-filtering techniques with statistical methods for error estimation. They are attractive because they can be integrated easily into conventional Monte Carlo rendering frameworks, they are applicable to most rendering effects, and their computational overhead is modest.