The small‐island effect (SIE), i.e. the hypothesis that species richness on islands below a certain threshold area varies independently of area, has become more and more part of the theoretical framework of biogeography and biodiversity research. However, existing SIE studies are extraordinarily biased taxonomically: plants and other animal taxonomic groups are predominantly studied, while birds are almost completely overlooked. Furthermore, previous methods for the detection of SIE are flawed in one or another way, including not accounting for model complexity, not comparing all relevant models, not including islands with no species, and ignoring the effects of logarithmic data transformations and habitat diversity in generating SIE. Therefore, the existence and the prevalence of the SIE may be dubious. In this study, after controlling for all these methodological shortcomings in detecting the SIE, we test for the existence of the SIE using bird data collected on islands in the Thousand Island Lake, China. We used the line‐transect method to survey bird occupancy and abundance on 42 islands from 2007 to 2011. We used three broad sets of analyses, regression‐based analyses, path analyses and null model analyses, to overcome potential methodological problems in detecting the SIE. We found no evidence for an SIE in avian communities in the Thousand Island Lake. Model selection based on AICc identified the simple power model without SIE as the most parsimonious model. In contrast, there was little support for the three breakpoint regression models with SIE. Path analyses and null model analyses also did not detect an SIE. We conclude that, for the robust detection of SIE, future study should carefully take all these methodological pitfalls into account.

Interactions between side chains of polymers have been utilized to tune the thermal and mechanical properties of polymeric materials. In liquid crystal (LC) elastomers (LCEs), previous studies have demonstrated that the configuration of LC monomers, specifically oblate or prolate, determines the direction of macroscopic material deformations relative to the orientational ordering of the LC functional groups. However, the effects of the copolymerization between different configurations of LC monomers on the phase behaviors and thermomechanical properties of LCEs have not been explored. Here, we reveal that statistically random copolymers of LC monomers with different configurations destabilize the orientational order of the LC functional groups, whereas the random insertion of LC monomers with the same configuration preserves the packing of the constituent mesogenic functional groups. We further demonstrate how this fundamental understanding can be applied to control both the direction and magnitude of the thermally triggered mechanical deformations of LC copolymer networks.

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Background: Cotton is an important fiber crop worldwide. The yield potential of current genotypes of cotton can be exploited through hybridization. However, to develop superior hybrids with high yield and fiber quality traits, information of genetic control of traits is prerequisite. Therefore, genetic analysis plays pivotal role in plant breeding. Results: In present study, North Carolina II mating design was used to cross 5 female parents with 6 male parents to produce 30 intraspecific F 1 cotton hybrids. All plant materials were tested in three different ecological regions of China during the year of 2016-2017. Additive-dominance-environment (ADE) genetic model was used to estimate the genetic effects and genotypic and phenotypic correlation of yield and fiber quality traits. Results showed that yield traits except lint percentage were mainly controlled by genetic and environment interaction effects, whereas lint percentage and fiber quality traits were determined by main genetic effects. Moreover, dominant and additiveenvironment interaction effects had more influence on yield traits, whereas additive and dominance-environment interaction effects were found to be predominant for fiber traits. Broad-sense and its interaction heritability were significant for all yield and most of fiber quality traits. Narrow-sense and its interaction heritability were non-significant for boll number and seed cotton yield. Correlation analysis indicated that seed cotton yield had significant positive correlation with other yield attributes and non-significant with fiber quality traits. All fiber quality traits had significant positive correlation with each other except micronaire. Conclusions: Results of current study provide important information about genetic control of yield and fiber quality traits. Further, this study identified that parental lines, e.g., SJ48-1, ZB-1, 851-2, and DT-8 can be utilized to improve yield and fiber quality traits in cotton.

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