The expected effects of climate change on wheat development

Air temperature and the atmospheric concentrations of carbon dioxide are expected to rise. These two factor have a great potential to affect development, growth and yield of crops, including wheat. Rising air temperature may affect wheat development more than rising atmospheric CO₂ as there is not yet evidence that elevated CO₂ concentrations can directly induce changes in wheat development. In winter wheat, temperature has a complex effect on development due to its strong interaction with vernalization and photoperiod. In this paper, potential effects of rising temperature on the development of winter wheat from sowing to heading are considered in the light of this complex controlling mechanism. Data from a large series of field trials made in Romania is analysed at first and, subsequently, the IATA-Wheat Phenology model is used to calculate the impact of air warming on wheat development under different climate change scenarios. Data from the field trials showed very clearly the occurrence of a complex temperature/photoperiod/vernalization interaction for field sown crops and demostrated that the photoperiodic and vernalization responses have a key role in controlling the duration of the emergence-heading period. Temperature plays, instead, a central role in controlling seed germination and crop emergence as well as leaf inititiation and leaf appearance rate. The results of model analysis showed very well that the impact of an even or uneven distribution of warning effects may be very different. In the first case, the model predicted that the duration of the vegetative period was at least partly reduced in some years. In the second case, the model suggested that if warming will be more pronounced in winter than in spring, as predicted for some areas of the world by General Circulation Models, we may expect an increase in the duration of the vegetative phase of growth. On the contrary, in case of a spring warming but unchanged winter temperatures, we may expect a substantial decrease in the duration of the vegetative period.     

A stochastic metapopulation model accounting for habitat dynamics

A stochastic metapopulation model accounting for habitat dynamics is presented. This is the stochastic SIS logistic model with the novel aspect that it incorporates varying carrying capacity. We present results of Kurtz and Barbour, that provide deterministic and diffusion approximations for a wide class of stochastic models, in a form that most easily allows their direct application to population models. These results are used to show that a suitably scaled version of the metapopulation model converges, uniformly in probability over finite time intervals, to a deterministic model previously studied in the ecological literature. Additionally, they allow us to establish a bivariate normal approximation to the quasi-stationary distribution of the process. This allows us to consider the effects of habitat dynamics on metapopulation modelling through a comparison with the stochastic SIS logistic model and provides an effective means for modelling metapopulations inhabiting dynamic landscapes.     

Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes?

Aim Species distribution models and geographical information system (GIS) technologies are becoming increasingly important tools in conservation planning and decision‐making. Often the rich data bases of museums and herbaria serve as the primary data for predicting species distributions. Yet key assumptions about the primary data often are untested, and violation of such assumptions may have consequences for model predictions. For example, users of primary data assume that sampling has been random with respect to geography and environmental gradients. Here we evaluate the assumption that plant voucher specimens adequately sample the climatic gradient and test whether violation of this assumption influences model predictions. Location Bolivia and Ecuador. Methods Using 323,711 georeferenced herbarium collections and nine climatic variables, we predicted the distribution of 76 plant species using maximum entropy models (MAXENT) with training points that sampled the climate environments randomly and training points that reflected the climate bias in the herbarium collections. To estimate the distribution of species, MAXENT finds the distribution of maximum entropy (i.e. closest to uniform) subject to the constraint that the expected value for each environmental variable under the estimated distribution matches its empirical average. The experimental design included species that differed in geographical range and elevation; all species were modelled with 20 and 100 training points. We examined the influence of the number of training points and climate bias in training points, elevation and range size on model performance using analysis of variance models. Results We found that significant parts of the climatic gradient were poorly represented in herbarium collections for both countries. For the most part, existing climatic bias in collections did not greatly affect distribution predictions when compared with an unbiased data set. Although the effects of climate bias on prediction accuracy were found to be greater where geographical ranges were characterized by high spatial variation in the degree of climate bias (i.e. ranges where the bias of the various climates sampled by collections deviated considerably from the mean bias), the greatest influence on model performance was the number of presence points used to train the model. Main conclusions These results demonstrate that predictions of species distributions can be quite good despite existing climatic biases in primary data found in natural history collections, if a sufficiently large number of training points is available. Because of consistent overprediction of models, these results also confirm the importance of validating models with independent data or expert opinion. Failure to include independent model validation, especially in cases where training points are limited, may potentially lead to grave errors in conservation decision‐making and planning.     

Multispectroscopic insight,morphological analysis and molecular docking studies of CuII-based chemotherapeutic drug entity with human serum albumin (HSA) and bovine serum albumin (BSA)

The interaction studies of Cu^II nalidixic acid–DACH chemotherapeutic drug entity, [C₃₆H₅₀N₈O₆Cu] with serum albumin proteins, viz., human serum albumin (HSA) and bovine serum albumin (BSA) employing UV–vis, fluorescence, CD, FTIR and molecular docking techniques have been carried out. Complex [C₃₆H₅₀N₈O₆Cu] demonstrated strong binding affinity towards serum albumin proteins via hydrophobic contacts with binding constants, K?=?3.18?×?10⁵ and 7.44?×?10⁴ M^–1 for HSA and BSA, respectively implicating a higher binding affinity for HSA. The thermodynamic parameters ΔG, ΔH and ΔS at different temperatures were also calculated and the interaction of complex [C₃₆H₅₀N₈O₆Cu] with HSA and BSA was found to be enthalpy and entropy favoured, nevertheless, complex [C₃₆H₅₀N₈O₆Cu] demonstrated higher binding affinity towards HSA than BSA evidenced from its higher binding constant values. Time resolved fluorescence spectroscopy (TRFS) was carried out to validate the static quenching mechanism of HSA/BSA fluorescence. The collaborative results of spectroscopic studies indicated that the microenvironment and the conformation of HSA and BSA (α–helix) were significantly perturbed upon interaction with complex [C₃₆H₅₀N₈O₆Cu]. Hirshfeld surfaces analysis and fingerprint plots revealed various intermolecular interactions viz., N–H····O, O–H····O and C–H····O linkages in a 2–dimensional framework that provide crucial information about the supramolecular architectures in the complex. Molecular docking studies were carried out to ascertain the preferential binding mode and affinity of complex [C₃₆H₅₀N₈O₆Cu] at the target site of HSA and BSA. Furthermore, only for Transmission electroscopy microscopy micrographs of HSA and BSA in presence of complex [C₃₆H₅₀N₈O₆Cu] revealed major protein morphological transitions and aggregation which validates efficient delivery of complex by serum proteins to the target site.

Communicated by Ramaswamy H. Sarma     

Early tetrapodomorph biogeography: Controlling for fossil record bias in macroevolutionary analyses

The fossil record provides direct empirical data for understanding macroevolutionary patterns and processes. Inherent biases in the fossil record are well known to confound analyses of this data. Sampling bias proxies have been used as covariates in regression models to test for such biases. Proxies, such as formation count, are associated with paleobiodiversity, but are insufficient for explaining species dispersal owing to a lack of geographic context. Here, we develop a sampling bias proxy that incorporates geographic information and test it with a case study on early tetrapodomorph biogeography. We use recently-developed Bayesian phylogeographic models and a new supertree of early tetrapodomorphs to estimate dispersal rates and ancestral habitat locations. We find strong evidence that geographic sampling bias explains supposed radiations in dispersal rate (potential adaptive radiations). Our study highlights the necessity of accounting for geographic sampling bias in macroevolutionary and phylogenetic analyses and provides an approach to test for its effect.     

Land use and climate influences on waterbirds in the Prairie Potholes

Aim We examined the influences of regional climate and land‐use variables on mallard (Anas platyrhynchos), blue‐winged teal (Anas discors), ruddy duck (Oxyura jamaicensis) and pied‐billed grebe (Podilymbus podiceps) abundances to inform conservation planning in the Prairie Pothole Region of the United States. Location The US portion of Bird Conservation Region 11 (US‐BCR11, the Prairie Potholes), which encompasses six states within the United States: Montana, North Dakota, South Dakota, Nebraska, Minnesota and Iowa. Methods We used data from the North American Breeding Bird Survey (NABBS), the National Land Cover Data Set, and the National Climatic Data Center to model the effects of environmental variables on waterbird abundance. We evaluated land‐use covariates at three logarithmically related spatial scales (1000, 10,000 and 100,000 ha), and constructed hierarchical spatial count models a priori using information from published habitat associations. Model fitting was performed using a hierarchical modelling approach within a Bayesian framework. Results Models with the same variables expressed at different scales were often in the best model subset, indicating that the influence of spatial scale was small. Both land‐use and climate variables contributed strongly to predicting waterbird abundance in US‐BCR11. The strongest positive influences on waterbird abundance were the percentage of wetland area across all three spatial scales, herbaceous vegetation and precipitation variables. Other variables that we included in our models did not appear to influence waterbirds in this study. Main conclusions Understanding the relationships of waterbird abundance to climate and land use may allow us to make predictions of future distribution and abundance as environmental factors change. Additionally, results from this study can suggest locations where conservation and management efforts should be focused.     

A dynamic, architectural plant model simulating resource-dependent growth

BACKGROUND AND AIMS: Physiological and architectural plant models have originally been developed for different purposes and therefore have little in common, thus making combined applications difficult. There is, however, an increasing demand for crop models that simulate the genetic and resource-dependent variability of plant geometry and architecture, because man is increasingly able to transform plant production systems through combined genetic and environmental engineering. MODEL: GREENLAB is presented, a mathematical plant model that simulates interactions between plant structure and function. Dual-scale automaton is used to simulate plant organogenesis from germination to maturity on the basis of organogenetic growth cycles that have constant thermal time. Plant fresh biomass production is computed from transpiration, assuming transpiration efficiency to be constant and atmospheric demand to be the driving force, under non-limiting water supply. The fresh biomass is then distributed among expanding organs according to their relative demand. Demand for organ growth is estimated from allometric relationships (e.g. leaf surface to weight ratios) and kinetics of potential growth rate for each organ type. These are obtained through parameter optimization against empirical, morphological data sets by running the model in inverted mode. Potential growth rates are then used as estimates of relative sink strength in the model. These and other 'hidden' plant parameters are calibrated using the non-linear, least-square method. KEY RESULTS AND CONCLUSIONS: The model reproduced accurately the dynamics of plant growth, architecture and geometry of various annual and woody plants, enabling 3D visualization. It was also able to simulate the variability of leaf size on the plant and compensatory growth following pruning, as a result of internal competition for resources. The potential of the model's underlying concepts to predict the plant's phenotypic plasticity is discussed.     

参数仪器变量估计近似分布方差的一种算法

本文对更一般的结构模型给出了参数的一种常用的仪器变量估计近似分布方差的一种算法．并且给出了未知真值x服从指数分布的例子．此算法对生物科学中统计规律的探讨有一定的应用价值．     

Does freshwater macroinvertebrate diversity along a pH‐gradient reflect adaptation to low pH?

1. The impacts of anthropogenic surface water acidification are much better known than those of natural acidity. Recent studies have indicated biodiversity is not degraded and species composition unaltered in naturally acidic compared to circumneutral watercourses.
2. Here, we use a geographically extensive dataset comprising sites in more than 200 Swedish streams to test whether the lack of effects on macroinvertebrate species diversity is due to exaptation and adaptation to natural acidity.
3. To this end, we modelled pH associated with spring flood episodes, which inflict the most challenging hydrochemical conditions to the biota. We compared taxonomic richness and species composition along the modelled pH gradient in northern Sweden, where acidity is largely natural, with southern Sweden, a region influenced by significant anthropogenic acidification.
4. We found Plecoptera richness did not respond to varying pH either in northern or southern Sweden. Ephemeroptera richness was sensitive to pH in both regions, while that of Trichoptera increased with increasing pH in southern Sweden, but decreased in the north. The taxonomic composition of Plecoptera changed along the pH gradient in both regions, whereas that of Ephemeroptera and Trichoptera changed more strongly with pH in southern Sweden.
5. Our results support the hypothesis that stream invertebrates are able to tolerate low pH through exaptation or adaptation, but that this capability varies among taxonomic groups.     

Detecting the coevolution of biosequences--an example of RNA interaction prediction

A probabilistic graphical model is proposed in order to detect the coevolution between different sites in biological sequences. The model extends the continuous-time Markov process of sequence substitution for single nucleic or amino acids and imposes general constraints regarding simultaneous changes on the substitution rate matrix. Given a multiple sequence alignment for each molecule of interest and a phylogenetic tree, the model can predict potential interactions within or between nucleic acids and proteins. Initial validation of the model is carried out using tRNA and 16S rRNA sequence data. The model accurately identifies the secondary interactions of tRNA as well as several known tertiary interactions. In addition, results on 16S rRNA data indicate this general and simple coevolutionary model outperforms several other parametric and nonparametric methods in predicting secondary interactions. Furthermore, the majority of the putative predictions exhibit either direct contact or proximity of the nucleotide pairs in the 3-dimensional structure of the Thermus thermophilus ribosomal small subunit. The results on RNA data suggest a general model of coevolution might be applied to other types of interactions between protein, DNA, and RNA molecules.