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.     

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.

     

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.     

Productivity–diversity patterns in arctic tundra vegetation

Productivity has long been argued to be a major driver of species richness patterns. In the present study we test alternative productivity–diversity hypotheses using vegetation data from the vast Eurasian tundra. The productivity–species pool hypothesis predicts positive relationships at both fine and coarse grain sizes, whereas the productivity–interaction hypothesis predicts unimodal patterns at fine grain size, and monotonic positive patterns at coarse grain size. We furthermore expect to find flatter positive (productivity–species pool hypothesis) or more strongly negative (productivity–interaction hypothesis) relationships for lichens and bryophytes than for vascular plants, because as a group, lichens and bryophytes are better adapted to extreme arctic conditions and more vulnerable to competition for light than the taller‐growing vascular plants. The normalised difference vegetation index (NDVI) was used as a proxy of productivity. The generally unimodal productivity–diversity patterns were most consistent with the productivity–interaction hypothesis. There was a general trend of decreasing species richness from moderately to maximally productive tundra, in agreement with an increasing importance of competitive interactions. High richness of vascular plants and lichens occurred in moderately low productive tundra areas, whereas that of bryophytes occurred in the least productive tundra habitats covered by this study. The fine and coarse grain richness trends were surprisingly uniform and no variation in beta diversity along the productivity gradient was seen for vascular plants or bryophytes. However, lichen beta diversity varied along the productivity gradient, probably reflecting their sensitivity to habitat conditions and biotic interactions. Overall, the results show evidence that productivity–diversity gradients exist in tundra and that these appear to be largely driven by competitive interactions. Our results also imply that climate warming‐driven increases in productivity will strongly affect arctic plant diversity patterns.     

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.     

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.     

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.     

Recent vegetation changes at the high‐latitude tree line ecotone are controlled by geomorphological disturbance,productivity and diversity

Aim We test how productivity, disturbance rate, plant functional composition and species richness gradients control changes in the composition of high‐latitude vegetation during recent climatic warming. Location Northern Fennoscandia, Europe. Methods We resampled tree line ecotone vegetation sites sampled 26 years earlier. To quantify compositional changes, we used generalized linear models to test relationships between compositional changes and environmental gradients. Results Compositional changes in species abundances are positively related to the normalized difference vegetation index (NDVI)‐based estimate of productivity gradient and to geomorphological disturbance. Competitive species in fertile sites show the greatest changes in abundance, opposed to negligible changes in infertile sites. Change in species richness is negatively related to initial richness, whereas geomorphological disturbance has positive effects on change in richness. Few lowland species have moved towards higher elevations. Main conclusions The sensitivity of vegetation to climate change depends on a complex interplay between productivity, physical and biotic disturbances, plant functional composition and richness. Our results suggest that vegetation on productive sites, such as herb‐rich deciduous forests at low altitudes, is more sensitive to climate warming than alpine tundra vegetation where grazing may have strong buffering effects. Geomorphological disturbance promotes vegetation change under climatic warming, whereas high diversity has a stabilizing effect.