Enantioselective Dehydrochlorination of δ-Hexachlorocyclohexane and δ-Pentachlorocyclohexene by LinA1 and LinA2 from Sphingobium indicum B90A

δ-Hexachlorocyclohexane (δ-HCH), one of the prevalent isomers of technical HCH, was enantioselectively dehydrochlorinated by the dehydrochlorinases LinA1 and LinA2 from Sphingobium indicum B90A to the very same δ-pentachlorocyclohexene enantiomer. Racemic δ-pentachlorocyclohexene, however, was transformed with opposite enantioselectivities by the two enzymes. A transformation pathway based on an anti-1,2-elimination, followed by a syn-1,4-elimination and a subsequent syn-1,2-elimination is postulated.     

Using unclassified continuous remote sensing data to improve distribution models of red-listed plant species

Remote sensing (RS) data may play an important role in the development of cost-effective means for modelling, mapping, planning and conserving biodiversity. Specifically, at the landscape scale, spatial models for the occurrences of species of conservation concern may be improved by the inclusion of RS-based predictors, to help managers to better meet different conservation challenges. In this study, we examine whether predicted distributions of 28 red-listed plant species in north-eastern Finland at the resolution of 25 ha are improved when advanced RS-variables are included as unclassified continuous predictor variables, in addition to more commonly used climate and topography variables. Using generalized additive models (GAMs), we studied whether the spatial predictions of the distribution of red-listed plant species in boreal landscapes are improved by incorporating advanced RS (normalized difference vegetation index, normalized difference soil index and Tasseled Cap transformations) information into species-environment models. Models were fitted using three different sets of explanatory variables: (1) climate-topography only; (2) remote sensing only; and (3) combined climate-topography and remote sensing variables, and evaluated by four-fold cross-validation with the area under the curve (AUC) statistics. The inclusion of RS variables improved both the explanatory power (on average 8.1 % improvement) and cross-validation performance (2.5 %) of the models. Hybrid models produced ecologically more reliable distribution maps than models using only climate-topography variables, especially for mire and shore species. In conclusion, Landsat ETM+ data integrated with climate and topographical information has the potential to improve biodiversity and rarity assessments in northern landscapes, especially in predictive studies covering extensive and remote areas.     

Soil moisture's underestimated role in climate change impact modelling in low‐energy systems

Shifts in precipitation regimes are an inherent component of climate change, but in low‐energy systems are often assumed to be less important than changes in temperature. Because soil moisture is the hydrological variable most proximally linked to plant performance during the growing season in arctic‐alpine habitats, it may offer the most useful perspective on the influence of changes in precipitation on vegetation. Here we quantify the influence of soil moisture for multiple vegetation properties at fine spatial scales, to determine the potential importance of soil moisture under changing climatic conditions. A fine‐scale data set, comprising vascular species cover and field‐quantified ecologically relevant environmental parameters, was analysed to determine the influence of soil moisture relative to other key abiotic predictors. Soil moisture was strongly related to community composition, species richness and the occurrence patterns of individual species, having a similar or greater influence than soil temperature, pH and solar radiation. Soil moisture varied considerably over short distances, and this fine‐scale heterogeneity may contribute to offsetting the ecological impacts of changes in precipitation for species not limited to extreme soil moisture conditions. In conclusion, soil moisture is a key driver of vegetation properties, both at the species and community level, even in this low‐energy system. Soil moisture conditions represent an important mechanism through which changing climatic conditions impact vegetation, and advancing our predictive capability will therefore require a better understanding of how soil moisture mediates the effects of climate change on biota.     

Dwarf Shrubs Impact Tundra Soils: Drier,Colder, and Less Organic Carbon

In the tundra, woody plants are dispersing towards higher latitudes and altitudes due to increasingly favourable climatic conditions. The coverage and height of woody plants are increasing, which may influence the soils of the tundra ecosystem. Here, we use structural equation modelling to analyse 171 study plots and to examine if the coverage and height of woody plants affect the growing-season topsoil moisture and temperature (<?10 cm) as well as soil organic carbon stocks (<?80 cm). In our study setting, we consider the hierarchy of the ecosystem by controlling for other factors, such as topography, wintertime snow depth and the overall plant coverage that potentially influence woody plants and soil properties in this dwarf shrub-dominated landscape in northern Fennoscandia. We found strong links from topography to both vegetation and soil. Further, we found that woody plants influence multiple soil properties: the dominance of woody plants inversely correlated with soil moisture, soil temperature, and soil organic carbon stocks (standardised regression coefficients?=???0.39; ??0.22; ??0.34, respectively), even when controlling for other landscape features. Our results indicate that the dominance of dwarf shrubs may lead to soils that are drier, colder, and contain less organic carbon. Thus, there are multiple mechanisms through which woody plants may influence tundra soils.

     

Inclusion of soil data improves the performance of bioclimatic envelope models for insect species distributions in temperate Europe

Aim To analyse the effect of the inclusion of soil and land‐cover data on the performance of bioclimatic envelope models for the regional‐scale prediction of butterfly (Rhopalocera) and grasshopper (Orthoptera) distributions. Location Temperate Europe (Belgium). Methods Distributional data were extracted from butterfly and grasshopper atlases at a resolution of 5 km for the period 1991–2006 in Belgium. For each group separately, the well‐surveyed squares (n = 366 for butterflies and n = 322 for grasshoppers) were identified using an environmental stratification design and were randomly divided into calibration (70%) and evaluation (30%) datasets. Generalized additive models were applied to the calibration dataset to estimate occurrence probabilities for 63 butterfly and 33 grasshopper species, as a function of: (1) climate, (2) climate and land‐cover, (3) climate and soil, and (4) climate, land‐cover and soil variables. Models were evaluated as: (1) the amount of explained deviance in the calibration dataset, (2) Akaike’s information criterion, and (3) the number of omission and commission errors in the evaluation dataset. Results Information on broad land‐cover classes or predominant soil types led to similar improvements in the performance relative to the climate‐only models for both taxonomic groups. In addition, the joint inclusion of land‐cover and soil variables in the models provided predictions that fitted more closely to the species distributions than the predictions obtained from bioclimatic models incorporating only land‐cover or only soil variables. The combined models exhibited higher discrimination ability between the presence and absence of species in the evaluation dataset. Main conclusions These results draw attention to the importance of soil data for species distribution models at regional scales of analysis. The combined inclusion of land‐cover and soil data in the models makes it possible to identify areas with suitable climatic conditions but unsuitable combinations of vegetation and soil types. While contingent on the species, the results indicate the need to consider soil information in regional‐scale species–climate impact models, particularly when predicting future range shifts of species under climate change.     

Inclusion of local environmental conditions alters high-latitude vegetation change predictions based on bioclimatic models

Current predictions of how species will respond to climate change are typically based on coarse-grained climate surfaces utilizing bioclimate envelope modelling. However, the suitability of environmental conditions for a given species might result from a variety of factors including some unrelated to climate. To address this issue, we investigated whether the inclusion of topographical and soil information in bioclimatic envelope models would significantly alter predictions of climate change—induced fine-scale tree and shrub species range size changes at the tree-limit in subarctic Europe. Using generalized additive models and data on current climate and species distributions and three different climate scenarios for the period 2040–2069, we developed predictions of the currently suitable area and potential range size changes of seven tree and shrub species in an area of 1,100 km² at a resolution of 1-ha. The inclusion of topography and soil information increased the predictive accuracy of climate-only models for all studied species. The predicted changes in species distribution volumes were contradictory, and the predicted occurrences varied greatly depending on the model used. Our results therefore support the arguments that vegetation responses to climate change can be influenced by local environmental conditions and that attention should be paid to the combined effects of these factors. We conclude that disregarding local topography and soil conditions in bioclimatic models may result in biased projections of range expansions and the associated colonization, extinction and turnover assessments.     

Natural and experimental infection of Caenorhabditis nematodes by novel viruses related to nodaviruses

An ideal model system to study antiviral immunity and host-pathogen co-evolution would combine a genetically tractable small animal with a virus capable of naturally infecting the host organism. The use of C. elegans as a model to define host-viral interactions has been limited by the lack of viruses known to infect nematodes. From wild isolates of C. elegans and C. briggsae with unusual morphological phenotypes in intestinal cells, we identified two novel RNA viruses distantly related to known nodaviruses, one infecting specifically C. elegans (Orsay virus), the other C. briggsae (Santeuil virus). Bleaching of embryos cured infected cultures demonstrating that the viruses are neither stably integrated in the host genome nor transmitted vertically. 0.2 μm filtrates of the infected cultures could infect cured animals. Infected animals continuously maintained viral infection for 6 mo (～50 generations), demonstrating that natural cycles of horizontal virus transmission were faithfully recapitulated in laboratory culture. In addition to infecting the natural C. elegans isolate, Orsay virus readily infected laboratory C. elegans mutants defective in RNAi and yielded higher levels of viral RNA and infection symptoms as compared to infection of the corresponding wild-type N2 strain. These results demonstrated a clear role for RNAi in the defense against this virus. Furthermore, different wild C. elegans isolates displayed differential susceptibility to infection by Orsay virus, thereby affording genetic approaches to defining antiviral loci. This discovery establishes a bona fide viral infection system to explore the natural ecology of nematodes, host-pathogen co-evolution, the evolution of small RNA responses, and innate antiviral mechanisms.     

Designs for clinical trials with time-to-event outcomes based on stopping guidelines for lack of benefit

Background

We present the design, methods and population characteristics of a large community trial that assessed the efficacy of a weekly supplement containing vitamin A or beta-carotene, at recommended dietary levels, in reducing maternal mortality from early gestation through 12 weeks postpartum. We identify challenges faced and report solutions in implementing an intervention trial under low-resource, rural conditions, including the importance of population choice in promoting generalizability, maintaining rigorous data quality control to reduce inter- and intra- worker variation, and optimizing efficiencies in information and resources flow from and to the field.

Methods

This trial was a double-masked, cluster-randomized, dual intervention, placebo-controlled trial in a contiguous rural area of ~435 sq km with a population of ~650,000 in Gaibandha and Rangpur Districts of Northwestern Bangladesh. Approximately 120,000 married women of reproductive age underwent 5-weekly home surveillance, of whom ~60,000 were detected as pregnant, enrolled into the trial and gave birth to ~44,000 live-born infants. Upon enrollment, at ~ 9 weeks' gestation, pregnant women received a weekly oral supplement containing vitamin A (7000 ug retinol equivalents (RE)), beta-carotene (42 mg, or ~7000 ug RE) or a placebo through 12 weeks postpartum, according to prior randomized allocation of their cluster of residence. Systems described include enlistment and 5-weekly home surveillance for pregnancy based on menstrual history and urine testing, weekly supervised supplementation, periodic risk factor interviews, maternal and infant vital outcome monitoring, birth defect surveillance and clinical/biochemical substudies.

Results

The primary outcome was pregnancy-related mortality assessed for 3 months following parturition. Secondary outcomes included fetal loss due to miscarriage or stillbirth, infant mortality under three months of age, maternal obstetric and infectious morbidity, infant infectious morbidity, maternal and infant micronutrient status, fetal and infant growth and prematurity, external birth defects and postnatal infant growth to 3 months of age.

Conclusion

Aspects of study site selection and its "resonance" with national and rural qualities of Bangladesh, the trial's design, methods and allocation group comparability achieved by randomization, field procedures and innovative approaches to solving challenges in trial conduct are described and discussed. This trial is registered with http://Clinicaltrials.gov as protocol NCT00198822.     

Distance decay of similarity in freshwater communities: do macro‐ and microorganisms follow the same rules?

Aim An intensively debated issue in macroecology is whether unicellular organisms show biogeographic patterns different from those of macroorganisms. One aspect of this debate addresses beta diversity, that is, do microbial organisms exhibit distance‐decay patterns similar to those of macroorganisms? And if so, is the decay of community similarity caused by spatially limited dispersal or by niche‐related factors? We studied the community similarity of stream diatoms, macroinvertebrates and bryophytes across the same set of sites in relation to environmental and geographic distance. Location A geographical gradient of c. 1100 km in Finland. Methods We first identified the subset of environmental variables that produced the highest correlation with community similarities for each taxonomic group. Based on these variables, we used partial Mantel tests to separate the independent influences of environmental and geographical distance for distance decay of community similarity, separately for diatoms, bryophytes and macroinvertebrates. Finally, macroinvertebrates were divided into three groups based on their different dispersal categories and a partial Mantel test was used to assess whether each of these groups were differently affected by environmental versus geographic distance, i.e. is dispersal a key factor in tests of niche versus neutral models. Results The level of environmental control was by far the strongest for diatoms; however, all groups were controlled more by environmental factors than by limited dispersal. Macroinvertebrate species with low dispersal ability were significantly related to geographic distance, while more effective dispersers showed no relationship to geography but were instead strongly related to environmental distance. Main conclusions Our results suggest that patterns between macro‐ and microorganisms are not fundamentally different, but the level of environmental control varies according to dispersal ability. The relative importance of niche versus dispersal processes is not simply a function of organism size but other traits (e.g. life‐history type, dispersal capacity) may obscure this relationship.     

Evaluation of consensus methods in predictive species distribution modelling

Aim  Spatial modelling techniques are increasingly used in species distribution modelling. However, the implemented techniques differ in their modelling performance, and some consensus methods are needed to reduce the uncertainty of predictions. In this study, we tested the predictive accuracies of five consensus methods, namely Weighted Average (WA), Mean(All), Median(All), Median(PCA), and Best, for 28 threatened plant species.
Location  North-eastern Finland, Europe.
Methods  The spatial distributions of the plant species were forecasted using eight state-of-the-art single-modelling techniques providing an ensemble of predictions. The probability values of occurrence were then combined using five consensus algorithms. The predictive accuracies of the single-model and consensus methods were assessed by computing the area under the curve (AUC) of the receiver-operating characteristic plot.
Results  The mean AUC values varied between 0.697 (classification tree analysis) and 0.813 (random forest) for the single-models, and from 0.757 to 0.850 for the consensus methods. WA and Mean(All) consensus methods provided significantly more robust predictions than all the single-models and the other consensus methods.
Main conclusions  Consensus methods based on average function algorithms may increase significantly the accuracy of species distribution forecasts, and thus they show considerable promise for different conservation biological and biogeographical applications.