Testing the paradox of enrichment along a land use gradient in a multitrophic aboveground and belowground community

In the light of ongoing land use changes, it is important to understand how multitrophic communities perform at different land use intensities. The paradox of enrichment predicts that fertilization leads to destabilization and extinction of predator-prey systems. We tested this prediction for a land use intensity gradient from natural to highly fertilized agricultural ecosystems. We included multiple aboveground and belowground trophic levels and land use-dependent searching efficiencies of insects. To overcome logistic constraints of field experiments, we used a successfully validated simulation model to investigate plant responses to removal of herbivores and their enemies. Consistent with our predictions, instability measured by herbivore-induced plant mortality increased with increasing land use intensity. Simultaneously, the balance between herbivores and natural enemies turned increasingly towards herbivore dominance and natural enemy failure. Under natural conditions, there were more frequently significant effects of belowground herbivores and their natural enemies on plant performance, whereas there were more aboveground effects in agroecosystems. This result was partly due to the “boom-bust” behavior of the shoot herbivore population. Plant responses to herbivore or natural enemy removal were much more abrupt than the imposed smooth land use intensity gradient. This may be due to the presence of multiple trophic levels aboveground and belowground. Our model suggests that destabilization and extinction are more likely to occur in agroecosystems than in natural communities, but the shape of the relationship is nonlinear under the influence of multiple trophic interactions.

Physically-based Assessment of Hurricane Surge Threat under Climate Change

Storm surges are responsible for much of the damage and loss of life associated with landfalling hurricanes. Understanding how global warming will affect hurricane surges thus holds great interest. As general circulation models (GCMs) cannot simulate hurricane surges directly, we couple a GCM-driven hurricane model with hydrodynamic models to simulate large numbers of synthetic surge events under projected climates and assess surge threat, as an example, for New York City (NYC). Struck by many intense hurricanes in recorded history and prehistory, NYC is highly vulnerable to storm surges. We show that the change of storm climatology will probably increase the surge risk for NYC; results based on two GCMs show the distribution of surge levels shifting to higher values by a magnitude comparable to the projected sea-level rise (SLR). The combined effects of storm climatology change and a 1 m SLR may cause the present NYC 100-yr surge flooding to occur every 3–20 yr and the present 500-yr flooding to occur every 25–240 yr by the end of the century.United States. National Oceanic and Atmospheric Administration (Postdoctoral Fellowship Program)National Science Foundation (U.S.

Gene selection with multiple ordering criteria

BACKGROUND: A microarray study may select different differentially expressed gene sets because of different selection criteria. For example, the fold-change and p-value are two commonly known criteria to select differentially expressed genes under two experimental conditions. These two selection criteria often result in incompatible selected gene sets. Also, in a two-factor, say, treatment by time experiment, the investigator may be interested in one gene list that responds to both treatment and time effects. RESULTS: We propose three layer ranking algorithms, point-admissible, line-admissible (convex), and Pareto, to provide a preference gene list from multiple gene lists generated by different ranking criteria. Using the public colon data as an example, the layer ranking algorithms are applied to the three univariate ranking criteria, fold-change, p-value, and frequency of selections by the SVM-RFE classifier. A simulation experiment shows that for experiments with small or moderate sample sizes (less than 20 per group) and detecting a 4-fold change or less, the two-dimensional (p-value and fold-change) convex layer ranking selects differentially expressed genes with generally lower FDR and higher power than the standard p-value ranking. Three applications are presented. The first application illustrates a use of the layer rankings to potentially improve predictive accuracy. The second application illustrates an application to a two-factor experiment involving two dose levels and two time points. The layer rankings are applied to selecting differentially expressed genes relating to the dose and time effects. In the third application, the layer rankings are applied to a benchmark data set consisting of three dilution concentrations to provide a ranking system from a long list of differentially expressed genes generated from the three dilution concentrations. CONCLUSION: The layer ranking algorithms are useful to help investigators in selecting the most promising genes from multiple gene lists generated by different filter, normalization, or analysis methods for various objectives

Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity

Rising atmospheric CO2 concentrations ([CO2]) are expected to enhance photosynthesis and reduce crop water use1. However, there is high uncertainty about the global implications of these effects for future crop production and agricultural water requirements under climate change. Here we combine results from networks of field experiments1, 2 and global crop models3 to present a spatially explicit global perspective on crop water productivity (CWP, the ratio of crop yield to evapotranspiration) for wheat, maize, rice and soybean under elevated [CO2] and associated climate change projected for a high-end greenhouse gas emissions scenario. We find CO2 effects increase global CWP by 10[0;47]%–27[7;37]% (median[interquartile range] across the model ensemble) by the 2080s depending on crop types, with particularly large increases in arid regions (by up to 48[25;56]% for rainfed wheat). If realized in the fields, the effects of elevated [CO2] could considerably mitigate global yield losses whilst reducing agricultural consumptive water use (4–17%). We identify regional disparities driven by differences in growing conditions across agro-ecosystems that could have implications for increasing food production without compromising water security. Finally, our results demonstrate the need to expand field experiments and encourage greater consistency in modelling the effects of rising [CO2] across crop and hydrological modelling communities

Impacts of climate change on plant diseases – opinions and trends

There has been a remarkable scientific output on the topic of how climate change is likely to affect plant diseases in the coming decades. This review addresses the need for review of this burgeoning literature by summarizing opinions of previous reviews and trends in recent studies on the impacts of climate change on plant health. Sudden Oak Death is used as an introductory case study: Californian forests could become even more susceptible to this emerging plant disease, if spring precipitations will be accompanied by warmer temperatures, although climate shifts may also affect the current synchronicity between host cambium activity and pathogen colonization rate. A summary of observed and predicted climate changes, as well as of direct effects of climate change on pathosystems, is provided. Prediction and management of climate change effects on plant health are complicated by indirect effects and the interactions with global change drivers. Uncertainty in models of plant disease development under climate change calls for a diversity of management strategies, from more participatory approaches to interdisciplinary science. Involvement of stakeholders and scientists from outside plant pathology shows the importance of trade-offs, for example in the land-sharing vs. sparing debate. Further research is needed on climate change and plant health in mountain, boreal, Mediterranean and tropical regions, with multiple climate change factors and scenarios (including our responses to it, e.g. the assisted migration of plants), in relation to endophytes, viruses and mycorrhiza, using long-term and large-scale datasets and considering various plant disease control methods

Elevational Gradients in Bird Diversity in the Eastern Himalaya: An Evaluation of Distribution Patterns and Their Underlying Mechanisms

BACKGROUND: Understanding diversity patterns and the mechanisms underlying those patterns along elevational gradients is critically important for conservation efforts in montane ecosystems, especially those that are biodiversity hotspots. Despite recent advances, consensus on the underlying causes, or even the relative influence of a suite of factors on elevational diversity patterns has remained elusive. METHODS AND PRINCIPAL FINDINGS: We examined patterns of species richness, density and range size distribution of birds, and the suite of biotic and abiotic factors (primary productivity, habitat variables, climatic factors and geometric constraints) that governs diversity along a 4500-m elevational gradient in the Eastern Himalayan region, a biodiversity hotspot within the world's tallest mountains. We used point count methods for sampling birds and quadrats for estimating vegetation at 22 sites along the elevational gradient. We found that species richness increased to approximately 2000 m, then declined. We found no evidence that geometric constraints influenced this pattern, whereas actual evapotranspiration (a surrogate for primary productivity) and various habitat variables (plant species richness, shrub density and basal area of trees) accounted for most of the variation in bird species richness. We also observed that ranges of most bird species were narrow along the elevation gradient. We find little evidence to support Rapoport's rule for the birds of Sikkim region of the Himalaya. CONCLUSIONS AND SIGNIFICANCE: This study in the Eastern Himalaya indicates that species richness of birds is highest at intermediate elevations along one of the most extensive elevational gradients ever examined. Additionally, primary productivity and factors associated with habitat accounted for most of the variation in avian species richness. The diversity peak at intermediate elevations and the narrow elevational ranges of most species suggest important conservation implications: not only should mid-elevation areas be conserved, but the entire gradient requires equal conservation attention

Frequency-dependent selection predicts patterns of radiations and biodiversity

Most empirical studies support a decline in speciation rates through time, although evidence for constant speciation rates also exists. Declining rates have been explained by invoking niche-filling processes, whereas constant rates have been attributed to non-adaptive processes such as sexual selection, mutation, and dispersal. Trends in speciation rate and the processes underlying it remain unclear, representing a critical information gap in understanding patterns of global diversity. Here we show that the speciation rate is driven by frequency dependent selection. We used a frequency-dependent and DNA sequence-based model of populations and genetic-distance-based speciation, in the absence of adaptation to ecological niches. We tested the frequency-dependent selection mechanism using cichlid fish and Darwin's finches, two classic model systems for which speciation rates and richness data exist. Using negative frequency dependent selection, our model both predicts the declining speciation rate found in cichlid fish and explains their species richness. For groups like the Darwin's finches, in which speciation rates are constant and diversity is lower, the speciation rate is better explained by a model without frequency-dependent selection. Our analysis shows that differences in diversity are driven by larger incipient species abundance (and consequent lower extinction rates) with frequency-dependent selection. These results demonstrate that mutations, genetic-distance-based speciation, sexual and frequency-dependent selection are sufficient not only for promoting rapid proliferation of new species, but also for maintaining the high diversity observed in natural systems

Predictive factors for overactive bladder symptoms after pelvic organ prolapse surgery

Contains fulltext : 89696.pdf (publisher's version ) (Closed access)INTRODUCTION AND HYPOTHESIS: This study focussed on the factors which predict the presence of symptoms of overactive bladder (OAB) after surgery for pelvic organ prolapse (POP). METHODS: Consecutive women who underwent POP surgery with or without the use of vaginal mesh materials in the years 2004-2007 were included. Assessments were made preoperatively and at follow-up, including physical examination (POP-Q) and standardised questionnaires (IIQ, UDI and DDI). RESULTS: Five hundred and five patients were included with a median follow-up of 12.7 (6-35) months. Bothersome OAB symptoms decreased after POP surgery. De novo bothersome OAB symptoms appeared in 5-6% of the women. Frequency and urgency were more likely to improve as compared with urge incontinence and nocturia. The best predictor for the absence of postoperative symptoms was the absence of preoperative bothersome OAB symptoms. CONCLUSION: The absence of bothersome OAB symptoms preoperatively was the best predictor for the absence of postoperative symptoms.1 september 201

Pre-processing Agilent microarray data

<p>Abstract</p> <p>Background</p> <p>Pre-processing methods for two-sample long oligonucleotide arrays, specifically the Agilent technology, have not been extensively studied. The goal of this study is to quantify some of the sources of error that affect measurement of expression using Agilent arrays and to compare Agilent's Feature Extraction software with pre-processing methods that have become the standard for normalization of cDNA arrays. These include log transformation followed by loess normalization with or without background subtraction and often a between array scale normalization procedure. The larger goal is to define best study design and pre-processing practices for Agilent arrays, and we offer some suggestions.</p> <p>Results</p> <p>Simple loess normalization without background subtraction produced the lowest variability. However, without background subtraction, fold changes were biased towards zero, particularly at low intensities. ROC analysis of a spike-in experiment showed that differentially expressed genes are most reliably detected when background is not subtracted. Loess normalization and no background subtraction yielded an AUC of 99.7% compared with 88.8% for Agilent processed fold changes. All methods performed well when error was taken into account by t- or z-statistics, AUCs ≥ 99.8%. A substantial proportion of genes showed dye effects, 43% (99%<it>CI </it>: 39%, 47%). However, these effects were generally small regardless of the pre-processing method.</p> <p>Conclusion</p> <p>Simple loess normalization without background subtraction resulted in low variance fold changes that more reliably ranked gene expression than the other methods. While t-statistics and other measures that take variation into account, including Agilent's z-statistic, can also be used to reliably select differentially expressed genes, fold changes are a standard measure of differential expression for exploratory work, cross platform comparison, and biological interpretation and can not be entirely replaced. Although dye effects are small for most genes, many array features are affected. Therefore, an experimental design that incorporates dye swaps or a common reference could be valuable.</p