Karin Nadrowski

Parallel to the advent of social media and the easy access to online pornographic content there is a sharp increase in adolescent females expressing gender dysphoria worldwide. This paper argues that treatment of gender dysphoria in female adolescents must include explicit exploration into their use and exchange of pornographic content, as well as possible online or offline contacts with adults. Possible avenues of how pornographic content may increase the shame and fear of becoming a woman include the acquisition of misogynistic sexual scripts based on false assumptions on sexuality including the normalization of the violation of females as pleasurable for them, peer influence among female friendship groups, the susceptibility of our medical systems to "mass hysteria" phenomena, easier access of adults with sexually abusive intentions to youth through social media, sexual abuse and victim blaming on females, as well as the influence of pornography on mentalization capacities. As the influence of pornography on gender dysphoria in girls is understudied, this paper provides questions for qualitative and quantitative research, case studies and history taking. Especially the lack of an adequate other during exposure may aggravate false assumptions on gender roles and gender inequality seen in mainstream pornography. Girls affected by autism might be at higher risk because of their reduced mentalization capacities. Working through experiences associated with pornographic content and sexually abusive experiences may correct false beliefs about gender inequality and therefore might alleviate gender dysphoria.

Summary: Traits are powerful predictors of ecosystem functions pointing to underlying physiological and ecological processes. Plant individual performance results from the coordinated operation of many processes, ranging from nutrient uptake over organ turnover to photosynthesis, thus requiring a large set of traits for its prediction. For plant performance on higher hierarchical levels, e.g. populations, additional traits important for plant‐plant and trophic interactions may be required which should even enlarge the spectrum of relevant predictor traits. The goal of this study was to assess the importance of plant functional traits to predict individual and population performance of grassland species with particular focus on the significance of root traits. We tested this for 59 grassland species using 35 traits divided into three trait clusters: leaf traits (16), stature traits (8) and root traits (11), using individual biomass of mesocosm plants as a measure of individual performance and population biomass of monocultures as a measure of population performance. We applied structural equation models to disentangle direct effects of single traits on population biomass and indirect effects via individual plant biomass or shoot density.We tested multivariate trait effects on individual and population biomass to analyse whether the importance of different trait clusters shifts with increasing hierarchical integration from individuals to populations. Traits of all three clusters significantly correlated with individual and population biomass. However, in spite of a number of significant correlations, above‐below‐ground linkages were generally weak, with few exceptions like N content. Stature traits exclusively affected population biomass indirectly via their effect on individual biomass, whereas root and leaf traits showed also direct effects and partly indirect effects via density. The inclusion of root traits in multiple regression models improved the prediction of individual biomass when compared with models with only above‐ground information only slightly (95% vs. 93% of variance prediction with and without root traits, respectively) but was crucial for the prediction of population biomass (77% and 49%, respectively). Root traits were more important for plant performance than leaf traits and were even the most important predictors at the population level Synthesis. Upscaling from the individual to the population level reflects an increasing number of processes requiring traits from different trait clusters for their prediction. Our results emphasize the importance of root traits for trait‐based studies especially at higher organizational levels. Our approach provides a comprehensive framework acknowledging the hierarchical nature of trait influences. This is one step towards a more process‐oriented assessment of trait‐based approaches.

We are witnessing a growing gap separating primary research data from derived data products presented as knowledge in publications. Although journals today more often require the underlying data products used to derive the results as a prerequisite for a publication, the important link to the primary data is lost. However, documenting the postprocessing steps of data linking, the primary data with derived data products has the potential to increase the accuracy and the reproducibility of scientific findings significantly. Here, we introduce therBEFdata R package as companion to the collaborative data management platformBEFdata. The R package provides programmatic access to features of the platform. It allows to search for data and integrates the search with external thesauri to improve the data discovery. It allows to download and import data and metadata into R for analysis. A batched download is available as well which works along a paper proposal mechanism implemented byBEFdata. This feature ofBEFdata allows to group primary data and metadata and streamlines discussions and collaborations revolving around a certain research idea. The upload functionality of the R package in combination with the paper proposal mechanism of the portal allows to attach derived data products and scripts directly from R, thus addressing major aspects of documenting data postprocessing. We present the core features of therBEFdata R package along an ecological analysis example and further discuss the potential of postprocessing documentation for data, linking primary data with derived data products and knowledge.

Sharing scientific analyses via workflows has great potential to improve the reproducibility of science as well as communicating research results. This is particularly useful for trans-disciplinary research fields such as biodiversity - ecosystem functioning (BEF), where syntheses need to merge data ranging from genes to the biosphere. Here we argue that enabling simplicity in the very beginning of workflows, at the point of data description and merging, offers huge potentials in reducing workflow complexity and in fostering data and workflow reuse. We illustrate our points using a typical analysis in BEF research, the aggregation of carbon pools in a forest ecosystem. We introduce indicators for the complexity of workflow components including data sources. We show that workflow complexity decreases exponentially during the course of the analysis and that simple text-based measures help to identify bottlenecks in a workflow and group workflow components according to tasks. We thus suggest that focusing on simplifying steps of data aggregation and imputation will greatly improve workflow readability and thus reproducibility. Providing feedback to data providers about the complexity of their datasets may help to produce better focused data that can be used more easily in further studies. At the same time, providing feedback about the complexity of workflow components may help to exchange shorter and simpler workflows for easier reuse. Additionally, identifying repetitive tasks informs software development in providing automated solutions. We discuss current initiatives in software and script development that implement quality control for simplicity and social tools of script valuation. Taken together we argue that focusing on simplifying data sources and workflow components will improve and accelerate data and workflow reuse and simplify the reproducibility of data-driven science.

Summary: Environmental selection and dispersal limitation are two of the primary processes structuring biotic communities in ecosystems, but little is known about these processes in shaping soil microbial communities during secondary forest succession. We examined the communities of ectomycorrhizal (EM) fungi in young, intermediate and old forests in a Chinese subtropical ecosystem, using 454 pyrosequencing. The EM fungal community consisted of 393 operational taxonomic units (OTUs), belonging to 21 EM fungal lineages, in which three EM fungal lineages and 11 EM fungal OTUs showed significantly biased occurrence among the young, intermediate and old forests. The EM fungal community was structured by environmental selection and dispersal limitation in old forest, but only by environmental selection in young, intermediate, and whole forests. Furthermore, the EM fungal community was affected by different factors in the different forest successional stages, and the importance of these factors in structuring EM fungal community dramatically decreased along the secondary forest succession series. This study suggests that different assembly mechanisms operate on the EM fungal community at different stages in secondary subtropical forest succession.

Future climates are likely to include extreme events, which in turn have great impacts on ecological systems. In this study, we investigated possible effects that could mitigate stem breakage caused by a rare and extreme ice storm in a Chinese subtropical forest across a gradient of forest diversity. We used Bayesian modeling to correct stem breakage for tree size and variance components analysis to quantify the influence of taxon, leaf and wood functional traits, and stand level properties on the probability of stem breakage. We show that the taxon explained four times more variance in individual stem breakage than did stand level properties; trees with higher specific leaf area (SLA) were less susceptible to breakage. However, a large part of the variation at the taxon scale remained unexplained, implying that unmeasured or undefined traits could be used to predict damage caused by ice storms. When aggregated at the plot level, functional diversity and wood density increased after the ice storm. We suggest that for the adaption of forest management to climate change, much can still be learned from looking at functional traits at the taxon level.

Summary: Biodiversity–ecosystem functioning (BEF) experiments address ecosystem‐level consequences of species loss by comparing communities of high species richness with communities from which species have been gradually eliminated.BEFexperiments originally started with microcosms in the laboratory and with grassland ecosystems. A new frontier in experimentalBEFresearch is manipulating tree diversity in forest ecosystems, compelling researchers to think big and comprehensively. We present and discuss some of the major issues to be considered in the design ofBEFexperiments with trees and illustrate these with a new forest biodiversity experiment established in subtropicalChina (Xingangshan,JiangxiProvince) in 2009/2010. Using a pool of 40 tree species, extinction scenarios were simulated with tree richness levels of 1, 2, 4, 8 and 16 species on a total of 566 plots of 25·8 × 25·8 m each. The goal of this experiment is to estimate effects of tree and shrub species richness on carbon storage and soil erosion; therefore, the experiment was established on sloped terrain. The following important design choices were made: (i) establishing many small rather than fewer larger plots, (ii) using high planting density and random mixing of species rather than lower planting density and patchwise mixing of species, (iii) establishing a map of the initial ‘ecoscape’ to characterize site heterogeneity before the onset of biodiversity effects and (iv) manipulating tree species richness not only in random but also in trait‐oriented extinction scenarios. Data management and analysis are particularly challenging inBEFexperiments with their hierarchical designs nesting individuals within‐species populations within plots within‐species compositions. Statistical analysis best proceeds by partitioning these random terms into fixed‐term contrasts, for example, species composition into contrasts for species richness and the presence of particular functional groups, which can then be tested against the remaining random variation among compositions. We conclude that forestBEFexperiments provide exciting and timely research options. They especially require careful thinking to allow multiple disciplines to measure and analyse data jointly and effectively. Achieving specific research goals and synergy with previous experiments involves trade‐offs between different designs and requires manifold design decisions.

Numerous studies have reported positive effects of species richness on plant community productivity. Such biodiversity effects are usually quantified by comparing the performance of plant mixtures with reference monocultures. However, several mechanisms, such as the lack of resource complementarity and facilitation or the accumulation of detrimental agents, suggest that monocultures are more likely than mixtures to deteriorate over time. Increasing biodiversity effects over time could therefore result from declining monocultures instead of reflecting increases in the functioning of mixtures. Commonly, the latter is assumed when positive trends in biodiversity effects occur. Here, we analysed the performance of 60 grassland species growing in monocultures and mixtures over 9 years in a biodiversity experiment to clarify whether their temporal biomass dynamics differed and whether a potential decline of monocultures contributed significantly to the positive net biodiversity effect observed. Surprisingly, individual species’ populations produced, on average, significantly more biomass per unit area when growing in monoculture than when growing in mixture. Over time, productivity of species decreased at a rate that was, on average, slightly more negative in monocultures than in mixtures. The mean net biodiversity effect across all mixtures was continuously positive and ranged between 64–217 g per m². Short-term increases in the mean net biodiversity effect were only partly due to deteriorating monocultures and were strongly affected by particular species gaining dominance in mixtures in the respective years. We conclude that our species performed, on average, comparably in monocultures and mixtures; monoculture populations being slightly more productive than mixture populations but this trend decreased over time. This suggested that negative feedbacks had not yet affected monocultures strongly but could potentially become more evident in the future. Positive biodiversity effects on aboveground productivity were heavily driven by a small, but changing, set of species that behaved differently from the average species.