The contributions of programmed developmental change and phenotypic plasticity to within-individual variation in leaf traits in Dicerandra linearifolia

Variation among modules of a single genet could provide a means of adaptation to environmental heterogeneity. Two mechanisms that can give rise to such variation are programmed developmental change and phenotypic plasticity. I quantified the relative roles of these two mechanisms in causing within-individual variation in six leaf traits of an annual plant. Under controlled temperatures, morphological, anatomical, and physiological traits of leaves produced by the same individual differed as a function of both the node at which they were produced and the temperature they experienced during development. Temperature, node, and interactions between them all contributed significantly to the pattern of within-individual variation in leaf traits, although the relative contributions of programmed developmental change and phenotypic plasticity differed for different traits. I hypothesize that these two mechanisms for generating within-individual variation in module phenotype are favored by different patterns of environmental heterogeneity; when the sequence of environments encountered by modules of a single individual is predictable, programmed developmental change may be favored, and phenotypic plasticity may be favored when the sequence of environments is irregular with respect to individual ontogeny and therefore not predictable.     

Estimating switchgrass productivity in the Great Plains using satellite vegetation index and site environmental variables

Switchgrass is being evaluated as a potential feedstock source for cellulosic biofuels and is being cultivated in several regions of the United States. The recent availability of switchgrass land cover maps derived from the National Agricultural Statistics Service cropland data layer for the conterminous United States provides an opportunity to assess the environmental conditions of switchgrass over large areas and across different geographic locations. The main goal of this study is to develop a data-driven multiple regression switchgrass productivity model and identify the optimal climate and environment conditions for the highly productive switchgrass in the Great Plains (GP). Environmental and climate variables used in the study include elevation, soil organic carbon, available water capacity, climate, and seasonal weather. Satellite-derived growing season averaged Normalized Difference Vegetation Index (GSN) was used as a proxy for switchgrass productivity. Multiple regression analyses indicate that there are strong correlations between site environmental variables and switchgrass productivity (r = 0.95). Sufficient precipitation and suitable temperature during the growing season (i.e., not too hot or too cold) are favorable for switchgrass growth. Elevation and soil characteristics (e.g., soil available water capacity) are also an important factor impacting switchgrass productivity. An anticipated switchgrass biomass productivity map for the entire GP based on site environmental and climate conditions and switchgrass productivity model was generated. Highly productive switchgrass areas are mainly located in the eastern part of the GP. Results from this study can help land managers and biofuel plant investors better understand the general environmental and climate conditions influencing switchgrass growth and make optimal land use decisions regarding switchgrass development in the GP.     

Reply to the first systematic response by the Global Footprint Network to criticism: A real debate finally?

We respond to a reaction of the Global Footprint Network/GFN on our 8-point criticism of the ecological footprint. We also refer to, and comment on, an associated debate in this journal between Giampietro and Saltelli, 2014a, Giampietro and Saltelli, 2014b, on the one hand, and Goldfinger et al. (2014), on the other. We conclude that criticism on the footprint is accumulating and coherent across the various studies and disciplines and among the different authors. This was the first time that Wackernagel/GFN systematically responded to our criticisms. Hence, our response contains several original elements and the resulting exchange can be seen to add value to the existing literature. It ultimately allows readers to better make up their mind about the different viewpoints on the ecological footprint.     

Analysis of Rates Using a Generalized Poisson Regression Model

In this paper a generalization of the Poisson regression model indexed by a shape parameter is proposed for the analysis of life table and follow-up data with concomitant variables. The model is suitable for analysis of extra-Poisson variation data. The model is used to fit the survival data given in Holford (1980). The model parameters, the hazard and survival functions are estimated by the method of maximum likelihood. The results obtained from this study seem to be comparable to those obtained by Chen (1988). Approximate tests of the dispersion and goodness-of-fit of the data to the model are also discussed.     

Sleep architecture is related to the season of PSG recording in 8-month-old infants

ABSTRACT

To date, little is known about the impact of season on infant sleep. In higher latitudes, the duration of daily light time varies substantially between different seasons, and environmental light is one potential factor affecting sleep. In this cohort study, one-night polysomnography (PSG) was performed on 72 healthy 8-month-old infants in 2012 and 2013 to study the effect of season on the sleep architecture of young infants in Finland. The children were divided into four subgroups, according to the amount of light during their birth season and the amount of light during the season of the PSG recordings, corresponding to spring, summer, autumn, and winter. We found that the season of birth did not have an impact on the infants’ sleep architecture at 8 months of age, but the season of the PSG recording did have an effect on several sleep variables. In the PSGs conducted during the spring, there was less N3 sleep and more N2 sleep than in the PSGs conducted during the autumn. In addition, there was more fragmented sleep during spring than autumn. According to our data, the season has an effect on the sleep architecture of young infants and should, therefore, be considered when evaluating the PSG findings of young infants. The exact mechanisms behind this novel finding remain unclear, however. The findings imply that infants` sleep is affected by the season or light environment, as is the case in adult sleep. Since potential explanatory factors, such as direct natural or artificial light exposure and the melatonin levels of the infants, were not controlled, more research is needed in the future to better understand this phenomenon.     

Field Hypothesis on the Self-regulation of Gene Expression

The mechanism of the self-regulation of gene expression in living cells is generally explained by considering complicated networks of key-lock relationships, and in fact there is a large body of evidence on a hugenumber of key-lock relationships. However, in the present article we stress that with the network hypothesis alone it is impossible to fully explain the mechanism of self-regulation in life. Recently, it has been established that individual giant DNA molecules, larger than several tens of kilo base pairs, undergo a large discrete transition in their higher-order structure. It has become clear that nonspecific weak interactions with various chemicals, suchas polyamines, small salts, ATP and RNA, cause on/off switching in the higher-order structure of DNA. Thus, the field parameters of the cellular environment should play important roles in the mechanism of self-regulation, in addition to networks of key and locks. This conformational transition induced by field parameters may be related to rigid on/off regulation, whereas key-lock relationships may be involved in a more flexible control of gene expression.