Climate change and the optimal flowering time of annual plants in seasonal environments

Long‐term phenology monitoring has documented numerous examples of changing flowering dates during the last century. A pivotal question is whether these phenological responses are adaptive or not under directionally changing climatic conditions. We use a classic dynamic growth model for annual plants, based on optimal control theory, to find the fitness‐maximizing flowering time, defined as the switching time from vegetative to reproductive growth. In a typical scenario of global warming, with advanced growing season and increased productivity, optimal flowering time advances less than the start of the growing season. Interestingly, increased temporal spread in production over the season may either advance or delay the optimal flowering time depending on overall productivity or season length. We identify situations where large phenological changes are necessary for flowering time to remain optimal. Such changes also indicate changed selection pressures. In other situations, the model predicts advanced phenology on a calendar scale, but no selection for early flowering in relation to the start of the season. We also show that the optimum is more sensitive to increased productivity when productivity is low than when productivity is high. All our results are derived using a general, graphical method to calculate the optimal flowering time applicable for a large range of shapes of the seasonal production curve. The model can thus explain apparent maladaptation in phenological responses in a multitude of scenarios of climate change. We conclude that taking energy allocation trade‐offs and appropriate time scales into account is critical when interpreting phenological patterns.     

Evolutionary implications of the human aldolase-A, -B, -C, and -pseudogene chromosome locations.

The aldolase genes represent an ancient gene family with tissue-specific isozymic forms expressed only in vertebrates. The chromosomal locations of the aldolase genes provide insight into their tissue-specific and developmentally regulated expression and evolution. DNA probes for the human aldolase-A and -C genes and for an aldolase pseudogene were used to quantify and map the aldolase loci in the haploid human genome. Genomic hybridization of restriction fragments determined that all the aldolase genes exist in single copy in the haploid human genome. Spot-blot analysis of sorted chromosomes mapped human aldolase A to chromosome 16, aldolase C to chromosome 17, the pseudogene to chromosome 10; it previously had mapped the aldolase-B gene to chromosome 9. All loci are unlinked and located on to two pairs of morphologically similar chromosomes, a situation consistent with tetraploidization during isozymic and vertebrate evolution. Sequence comparisons of expressed and flanking regions support this conclusion. These locations on similar chromosome pairs correctly predicted that the aldolase pseudogene arose when sequences from the aldolase-A gene were inserted into the homologous aldolase location on chromosome 10.     

Mischaracterising density dependence biases estimated effects of coloured covariates on population dynamics

Environmental effects on population growth are often quantified by coupling environmental covariates with population time series, using statistical models that make particular assumptions about the shape of density dependence. We hypothesized that faulty assumptions about the shape of density dependence can bias estimated effect sizes of temporally autocorrelated covariates. We investigated the presence of bias using Monte Carlo simulations based on three common per capita growth functions with distinct density dependent forms (θ-Ricker, Ricker and Gompertz), autocorrelated (coloured) ‘known’ environmental covariates and uncorrelated (white) ‘unknown’ noise. Faulty assumptions about the shape of density dependence, combined with overcompensatory intrinsic population dynamics, can lead to strongly biased estimated effects of coloured covariates, associated with lower confidence interval coverage. Effects of negatively autocorrelated (blue) environmental covariates are overestimated, while those of positively autocorrelated (red) covariates can be underestimated, generally to a lesser extent. Prewhitening the focal environmental covariate effectively reduces the bias, at the expense of the estimate precision. Fitting models with flexible shapes of density dependence can also reduce bias, but increases model complexity and potentially introduces other problems of parameter identifiability. Model selection is a good option if an appropriate model is included in the set of candidate models. Under the specific and identifiable circumstances with high risk of bias, we recommend prewhitening or careful modelling of the shape of density dependence.     

An optimized grapevine RNA isolation procedure and statistical determination of reference genes for real-time RT-PCR during berry development

Background  

The response regulators represent the elements of bacterial two-component system and have been characterized from dicot plants like Arabidopsis but little information is available on the monocots, including the cereal crops. The aim of this study was to characterize type-A response regulator genes from rice, and to investigate their expression in various organs as well as in response to different hormones, including cytokinin, and environmental stimuli.     

UniCarb-DB: a database resource for glycomic discovery

Glycosylation is one of the most important post-translational modifications of proteins, known to be involved in pathogen recognition, innate immune response and protection of epithelial membranes. However, when compared to the tools and databases available for the processing of high-throughput proteomic data, the glycomic domain is severely lacking. While tools to assist the analysis of mass spectrometry (MS) and HPLC are continuously improving, there are few resources available to support liquid chromatography (LC)-MS/MS techniques for glycan structure profiling. Here, we present a platform for presenting oligosaccharide structures and fragment data characterized by LC-MS/MS strategies. The database is annotated with high-quality datasets and is designed to extend and reinforce those standards and ontologies developed by existing glycomics databases. AVAILABILITY: http://www.unicarb-db.org     

On observation distributions for state space models of population survey data

1.?State space models are starting to replace more simple time series models in analyses of temporal dynamics of populations that are not perfectly censused. By simultaneously modelling both the dynamics and the observations, consistent estimates of population dynamical parameters may be obtained. For many data sets, the distribution of observation errors is unknown and error models typically chosen in an ad-hoc manner. 2.?To investigate the influence of the choice of observation error on inferences, we analyse the dynamics of a replicated time series of red kangaroo surveys using a state space model with linear state dynamics. Surveys were performed through aerial counts and Poisson, overdispersed Poisson, normal and log-normal distributions may all be adequate for modelling observation errors for the data. We fit each of these to the data and compare them using AIC. 3.?The state space models were fitted with maximum likelihood methods using a recent importance sampling technique that relies on the Kalman filter. The method relaxes the assumption of Gaussian observation errors required by the basic Kalman filter. Matlab code for fitting linear state space models with Poisson observations is provided. 4.?The ability of AIC to identify the correct observation model was investigated in a small simulation study. For the parameter values used in the study, without replicated observations, the correct observation distribution could sometimes be identified but model selection was prone to misclassification. On the other hand, when observations were replicated, the correct distribution could typically be identified. 5.?Our results illustrate that inferences may differ markedly depending on the observation distributions used, suggesting that choosing an adequate observation model can be critical. Model selection and simulations show that for the models and parameter values in this study, a suitable observation model can typically be identified if observations are replicated. Model selection and replication of observations, therefore, provide a potential solution when the observation distribution is unknown.     

Genetic structure in the coral-reef-associated Banggai cardinalfish, Pterapogon kauderni

In this study, we used 11 polymorphic microsatellite loci to show that oceanic distances as small as 2-5 km are sufficient to produce high levels of population genetic structure (multilocus F(ST) as high as 0.22) in the Banggai cardinalfish (Pterapogon kauderni), a heavily exploited reef fish lacking a pelagic larval dispersal phase. Global F(ST) among all populations, separated by a maximum distance of 203 km, was 0.18 (R(ST) = 0.35). Moreover, two lines of evidence suggest that estimates of F(ST) may actually underestimate the true level of genetic structure. First, within-locus F(ST) values were consistently close to the theoretical maximum set by the average within-population heterozygosity. Second, the allele size permutation test showed that R(ST) values were significantly larger than F(ST) values, indicating that populations have been isolated long enough for mutation to have played a role in generating allelic variation among populations. The high level of microspatial structure observed in this marine fish indicates that life history traits such as lack of pelagic larval phase and a good homing ability do indeed play a role in shaping population genetic structure in the marine realm.     

A human cell surface receptor activated by free fatty acids and thiazolidinedione drugs

Fatty acids, which are essential nutritional components, are also involved in cardiovascular and metabolic diseases. Here we report a human cell surface receptor that we name free fatty acid receptor (FFAR), because it is specifically activated by medium to long-chain free fatty acids. The receptor belongs to the class of seven-transmembrane, G-protein coupled receptors (GPCRs) and also mediates responses to antidiabetic drugs of the thiazolidinedione type. It is expressed in skeletal muscle, heart, liver, and pancreatic beta-cells. Stimulation of FFAR increases the intracellular calcium concentration in cells expressing the receptor in a native (pancreatic beta-cell line) or in a recombinant form. In view of the nature of the activating substances, their physiological role in the body, and the tissue distribution of FFAR we suggest the term "nutrient sensing receptor" for receptors acting at the interface between dietary components and signalling molecules.     

A theory of stochastic harvesting in stochastic environments

We investigate how model populations respond to stochastic harvesting in a stochastic environment. In particular, we show that the effects of variable harvesting on the variance in population density and yield depend critically on the autocorrelation of environmental noise and on whether the endogenous dynamics of the population display over- or undercompensation to density. These factors interact in complicated ways; harvesting shifts the slope of the renewal function, and the net effect of this shift will depend on the sign and magnitude of the other influences. For example, when environmental noise exhibits a positive autocorrelation, the relative importance of a variable harvest to the variance in density increases with overcompensation but decreases with undercompensation. For a fixed harvesting level, an increasing level of autocorrelation in environmental noise will decrease the relative variation in population density when overcompensation would otherwise occur. These and other intricate interactions have important ramifications for the interpretation of time series data when no prior knowledge of demographic or environmental details exists. These effects are important whenever the harvesting rate is sufficiently high or variable, conditions likely to occur in many systems, whether the harvesting is caused by commercial exploitation or by any other strong agent of density-independent mortality.