Feeling moved by music: Investigating continuous ratings and acoustic correlates

The experience often described as feeling moved, understood chiefly as a social-relational emotion with social bonding functions, has gained significant research interest in recent years. Although listening to music often evokes what people describe as feeling moved, very little is known about the appraisals or musical features contributing to the experience. In the present study, we investigated experiences of feeling moved in response to music using a continuous rating paradigm. A total of 415 US participants completed an online experiment where they listened to seven moving musical excerpts and rated their experience while listening. Each excerpt was randomly coupled with one of seven rating scales (perceived sadness, perceived joy, feeling moved or touched, sense of connection, perceived beauty, warmth [in the chest], or chills) for each participant. The results revealed that musically evoked experiences of feeling moved are associated with a similar pattern of appraisals, physiological sensations, and trait correlations as feeling moved by videos depicting social scenarios (found in previous studies). Feeling moved or touched by both sadly and joyfully moving music was associated with experiencing a sense of connection and perceiving joy in the music, while perceived sadness was associated with feeling moved or touched only in the case of sadly moving music. Acoustic features related to arousal contributed to feeling moved only in the case of joyfully moving music. Finally, trait empathic concern was positively associated with feeling moved or touched by music. These findings support the role of social cognitive and empathic processes in music listening, and highlight the social-relational aspects of feeling moved or touched by music.

Drift on holey landscapes as a dominant evolutionary process

Phenotypes typically display integration, i.e. correlations between traits. For quantitative traits--like many behaviors, physiological processes, and life-history traits--patterns of integration are often assumed to have been shaped by the combination of linear, non-linear, and correlated selection, with trait correlations representative of optimal combinations. Unfortunately, this assumption has rarely been critically tested, in part due to a lack of clear alternatives. Here we show that trait integration across 6 phyla and 60 species (including both Plantae and Animalia) is consistent with evolution across high dimensional "holey landscapes" rather than classical models of selection. This suggests that the leading conceptualizations and modeling of the evolution of trait integration fail to capture how phenotypes are shaped. Instead, traits are integrated in a manner contrary to predictions of dominant evolutionary theory.

Trait-trait relationships and functional tradeoffs vary with genome size in prokaryotes

We report genomic traits that have been associated with the life history of prokaryotes and highlight conflicting findings concerning earlier observed trait correlations and tradeoffs. In order to address possible explanations for these contradictions we examined trait-trait variations of 11 genomic traits from ~ 17,000 sequenced genomes. The studied trait-trait variations suggested: (i) the predominance of two resistance and resilience-related orthogonal axes , (ii) an overlap between a resilience axis and an axis of resource usage efficiency. These findings imply that resistance associated traits of prokaryotes are globally decoupled from resilience and resource use efficiencies associated traits. However, further inspection of pairwise scatterplots showed that resistance and resilience traits tended to be positively related for genomes up to roughly five million base pairs and negatively for larger genomes. This in turn precludes a globally consistent assignment of prokaryote genomic traits to the competitor - stress-tolerator -ruderal (CSR) schema that sorts species depending on their location along disturbance and productivity gradients into three ecological strategies and may serve as an explanation for conflicting findings from earlier studies. All reviewed genomic traits featured significant phylogenetic signals and we propose that our trait table can be applied to extrapolate genomic traits from taxonomic marker genes. This will enable to empirically evaluate the assembly of these genomic traits in prokaryotic communities from different habitats and under different productivity and disturbance scenarios as predicted via the resistance-resilience framework formulated here.

The evolution of trait correlations constrains phenotypic adaptation to high CO 2 in a eukaryotic alga

Microbes form the base of food webs and drive biogeochemical cycling. Predicting the effects of microbial evolution on global elemental cycles remains a significant challenge due to the sheer number of interacting environmental and trait combinations. Here, we present an approach for integrating multivariate trait data into a predictive model of trait evolution. We investigated the outcome of thousands of possible adaptive walks parameterized using empirical evolution data from the alga Chlamydomonas exposed to high CO 2 . We found that the direction of historical bias (existing trait correlations) influenced both the rate of adaptation and the evolved phenotypes (trait combinations). Critically, we use fitness landscapes derived directly from empirical trait values to capture known evolutionary phenomena. This work demonstrates that ecological models need to represent both changes in traits and changes in the correlation between traits in order to accurately capture phytoplankton evolution and predict future shifts in elemental cycling.

Juvenile ecology drives adult morphology in two insect orders

Most animals undergo ecological niche shifts between distinct life phases, but such shifts can result in adaptive conflicts of phenotypic traits. Metamorphosis can reduce these conflicts by breaking up trait correlations, allowing each life phase to independently adapt to its ecological niche. This process is called adaptive decoupling. It is, however, yet unknown to what extent adaptive decoupling is realized on a macroevolutionary scale in hemimetabolous insects and if the degree of adaptive decoupling is correlated with the strength of ontogenetic niche shifts. It is also unclear whether the degree of adaptive decoupling is correlated with phenotypic disparity. Here, we quantify nymphal and adult trait correlations in 219 species across the whole phylogeny of earwigs and stoneflies to test whether juvenile and adult traits are decoupled from each other. We demonstrate that adult head morphology is largely driven by nymphal ecology, and that adult head shape disparity has increased with stronger ontogenetic niche shifts in some stonefly lineages. Our findings implicate that the hemimetabolan metamorphosis in earwigs and stoneflies does not allow for high degrees of adaptive decoupling, and that high phenotypic disparity can even be realized when the evolution of distinct life phases is coupled.

Predictive evolution of metabolic phenotypes using model-designed selection niches

Traits lacking fitness benefit cannot be directly selected for under Darwinian evolution. Thus, features such as metabolite secretion are currently inaccessible to adaptive laboratory evolution. Here, we utilize environment-dependency of trait correlations to enable Darwinian selection of fitness-neutral or costly traits. We use metabolic models to design selection niches and to identify surrogate traits that are genetically correlated with cell fitness in the selection niche but coupled to the desired trait in the target niche. Adaptive evolution in the selection niche and subsequent return to the target niche is thereby predicted to enhance the desired trait. We experimentally validate the theory by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds in wine fermentation. Genomic, transcriptomic, and proteomic changes in the evolved strains confirmed the predicted flux re-routing to aroma biosynthesis. The use of model-designed selection niches facilitates the predictive evolution of fitness-costly traits for ecological and biotechnological applications.

When does gap filling of trait data confound taxonomic and functional analyses?

<p>Data on plant traits are increasingly used to understand relationships between biodiversity and ecosystem processes. Large trait databases are sparse because they are compiled from many smaller and usually more local databases. This sparsity severely limits the potential for both multivariate and global data analyses, and so "gap-filling" (imputation) approaches are commonly used to predict missing trait data prior to analysis. Data imputation can result in large biases and circularity; yet, no best practice has evolved for the appropriate use of gap-filled data. Here, we use the TRY database, the largest global database of plant traits, in combination with the commonly used gap-filling algorithm, BayesianHierarchical Probabilistic Matrix Factorization (BHPMF), to address opportunities and problems introduced by gap-filling. BHPMF is the gap-filling method of choice for both TRY, and the large and widely used database sPLOT. It predicts missing trait data using the taxonomic hierarchy and observed patterns of trait variance and trait-trait correlations. We use three metrics: root mean square error estimates, coefficient of variation to assess univariate deviation, and silhouette indices to assess multivariate deviation and clustering strength. We show that gap-filling results in deviation of these metrics calculated for groupings at lower taxonomic levels (intra-specific and intra-genera), but less so at higher taxonomic levels (family) and for functional groups. Trait-trait correlations are preserved at all levels. The strength of deviations depends both on the percentage of gaps, and on data characteristics, e.g. intra-taxa variability. Gap-filling with dataset-external trait data generally ameliorates prediction error, but the deviations of intra-taxonomic variation measures depend on the content of the added data. We conclude that BHPMF gap-filling introduces little bias if specifically used for analyses of traits within functional groups, including growth forms and plant functional types (PFTs), as well as trait-trait correlations. However, we generally discourage their use for analyses of taxonomic groupings at or below the family level. In summary, our study supports decisions on when and how to integrate BHPMF gap-filled trait data in future studies. We conclude with selected best practices when using sparse databases.</p>