Long-term warming in a Mediterranean-type grassland affects soil bacterial functional potential but not bacterial taxonomic composition.

Climate warming is known to impact ecosystem composition and functioning. However, it remains largely unclear how soil microbial communities respond to long-term, moderate warming. In this study, we used Illumina sequencing and microarrays (GeoChip 5.0) to analyze taxonomic and functional gene compositions of the soil microbial community after 14 years of warming (at 0.8-1.0 °C for 10 years and then 1.5-2.0 °C for 4 years) in a Californian grassland. Long-term warming had no detectable effect on the taxonomic composition of soil bacterial community, nor on any plant or abiotic soil variables. In contrast, functional gene compositions differed between warming and control for bacterial, archaeal, and fungal communities. Functional genes associated with labile carbon (C) degradation increased in relative abundance in the warming treatment, whereas those associated with recalcitrant C degradation decreased. A number of functional genes associated with nitrogen (N) cycling (e.g., denitrifying genes encoding nitrate-, nitrite-, and nitrous oxidereductases) decreased, whereas nifH gene encoding nitrogenase increased in the warming treatment. These results suggest that microbial functional potentials are more sensitive to long-term moderate warming than the taxonomic composition of microbial community.

Whole genome phylogenies reflect the distributions of recombination rates for many bacterial species.

Although recombination is accepted to be common in bacteria, for many species robust phylogenies with well-resolved branches can be reconstructed from whole genome alignments of strains, and these are generally interpreted to reflect clonal relationships. Using new methods based on the statistics of single-nucleotide polymorphism (SNP) splits, we show that this interpretation is incorrect. For many species, each locus has recombined many times along its line of descent, and instead of many loci supporting a common phylogeny, the phylogeny changes many thousands of times along the genome alignment. Analysis of the patterns of allele sharing among strains shows that bacterial populations cannot be approximated as either clonal or freely recombining but are structured such that recombination rates between lineages vary over several orders of magnitude, with a unique pattern of rates for each lineage. Thus, rather than reflecting clonal ancestry, whole genome phylogenies reflect distributions of recombination rates.

Long-term nitrogen deposition increases heathland carbon sequestration.

The large increases in reactive nitrogen (N) deposition in developed countries since the Industrial Revolution have had a marked impact on ecosystem functioning, including declining species richness, shifts in species composition, and increased N leaching. A potential mitigation of these harmful effects is the action of N as a fertiliser, which, through increasing primary productivity (and subsequently, organic matter production), has the potential to increase ecosystem carbon (C) storage. Here we report the response of an upland heath to 10years of experimental N addition. We find large increases in plant and soil C and N pools, with N-driven C sequestration rates in the range of 13-138kgCkg-1. These rates are higher than those previously found in forest and lowland heath, mainly due to higher C sequestration in the litter layer. C sequestration is highest at lower N treatments (10, 20, and 40kgNha-1yr-1 above ambient), with evidence of saturation at the highest N treatment, reflecting a physiologically aged Calluna vulgaris (Calluna) canopy. To maintain these rates of sequestration, the Calluna canopy should be managed to maximise it's time in the building phase. Scaling our results across UK heathlands, this equates to an additional 0.77Mt CO2e per annum extra C sequestered into plant litter and the top 15cm of heathland soil as a result of N deposition. The bulk of this is found in the litter and organic soil horizons that hold an average of 23% and 54% of soil C, respectively. This additional C represents around 0.44% of UK annual anthropogenic GHG emissions. When considered in the context of falling biodiversity and altered species composition in heathland, policy focus should remain on reducing N emissions.

Predicting the Responses of Soil Nitrite-Oxidizers to Multi-Factorial Global Change: A Trait-Based Approach.

Soil microbial diversity is huge and a few grams of soil contain more bacterial taxa than there are bird species on Earth. This high diversity often makes predicting the responses of soil bacteria to environmental change intractable and restricts our capacity to predict the responses of soil functions to global change. Here, using a long-term field experiment in a California grassland, we studied the main and interactive effects of three global change factors (increased atmospheric CO2 concentration, precipitation and nitrogen addition, and all their factorial combinations, based on global change scenarios for central California) on the potential activity, abundance and dominant taxa of soil nitrite-oxidizing bacteria (NOB). Using a trait-based model, we then tested whether categorizing NOB into a few functional groups unified by physiological traits enables understanding and predicting how soil NOB respond to global environmental change. Contrasted responses to global change treatments were observed between three main NOB functional types. In particular, putatively mixotrophic Nitrobacter, rare under most treatments, became dominant under the 'High CO2+Nitrogen+Precipitation' treatment. The mechanistic trait-based model, which simulated ecological niches of NOB types consistent with previous ecophysiological reports, helped predicting the observed effects of global change on NOB and elucidating the underlying biotic and abiotic controls. Our results are a starting point for representing the overwhelming diversity of soil bacteria by a few functional types that can be incorporated into models of terrestrial ecosystems and biogeochemical processes.

State-space models' dirty little secrets: even simple linear Gaussian models can have estimation problems.

State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (Ursus maritimus) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results.

A Robust ANOVA Approach to Estimating a Phylogeny from Multiple Genes.

In this article, we address the issue of estimating the phylogenetic tree based on sequence data across a set of genes. Recognizing that the individual gene trees may not all share the same evolutionary history due to lateral gene transfer or differences in rates of evolution for instance, we develop a robust algorithm for tree estimation based on pairwise distances computed gene by gene. A robust analysis of variance (ANOVA) is used to combine the distances across all genes giving a summary distance for all genes. The tree can then be constructed using any distance method such as BIONJ. Using the weights from the robust ANOVA, we can then identify the outlying genes and taxa for further examination. As the method is based on distances, computation is much faster than maximum likelihood on the concatenated genes. It is also very straightforward to carry out a bootstrap analysis using standard methods for regression models. We test our methods in a comprehensive simulation study and apply them to three data sets recently analyzed in the literature.

Combining distance matrices on identical taxon sets for multi-gene analysis with singular value decomposition.

We present a simple and effective method for combining distance matrices from multiple genes on identical taxon sets to obtain a single representative distance matrix from which to derive a combined-gene phylogenetic tree. The method applies singular value decomposition (SVD) to extract the greatest common signal present in the distances obtained from each gene. The first right eigenvector of the SVD, which corresponds to a weighted average of the distance matrices of all genes, can thus be used to derive a representative tree from multiple genes. We apply our method to three well known data sets and estimate the uncertainty using bootstrap methods. Our results show that this method works well for these three data sets and that the uncertainty in these estimates is small. A simulation study is conducted to compare the performance of our method with several other distance based approaches (namely SDM, SDM* and ACS97), and we find the performances of all these approaches are comparable in the consensus setting. The computational complexity of our method is similar to that of SDM. Besides constructing a representative tree from multiple genes, we also demonstrate how the subsequent eigenvalues and eigenvectors may be used to identify if there are conflicting signals in the data and which genes might be influential or outliers for the estimated combined-gene tree.

Inertia in an ombrotrophic bog ecosystem in response to 9 years' realistic perturbation by wet deposition of nitrogen, separated by form.

Wet deposition of nitrogen (N) occurs in oxidized (nitrate) and reduced (ammonium) forms. Whether one form drives vegetation change more than the other is widely debated, as field evidence has been lacking. We are manipulating N form in wet deposition to an ombrotrophic bog, Whim (Scottish Borders), and here report nine years of results. Ammonium and nitrate were provided in rainwater spray as NH4 Cl or NaNO3 at 8, 24 or 56 kg N ha(-1)  yr(-1) , plus a rainwater only control, via an automated system coupled to site meteorology. Detrimental N effects were observed in sensitive nonvascular plant species, with higher cumulative N loads leading to more damage at lower annual doses. Cover responses to N addition, both in relation to form and dose, were species specific and mostly dependent on N dose. Some species were generally indifferent to N form and dose, while others were dose sensitive. Calluna vulgaris showed a preference for higher N doses as ammonium N and Hypnum jutlandicum for nitrate N. However, after 9 years, the magnitude of change from wet deposited N on overall species cover is small, indicating only a slow decline in key species. Nitrogen treatment effects on soil N availability were likewise small and rarely correlated with species cover. Ammonium caused most N accumulation and damage to sensitive species at lower N loads, but toxic effects also occurred with nitrate. However, because different species respond differently to N form, setting of ecosystem level critical loads by N form is challenging. We recommend implementing the lowest value of the critical load range where communities include sensitive nonvascular plants and where ammonium dominates wet deposition chemistry. In the context of parallel assessment at the same site, N treatments for wet deposition showed overall much smaller effects than corresponding inputs of dry deposition as ammonia.

Patterns of ecological specialization among microbial populations in the Red Sea and diverse oligotrophic marine environments.

Large swaths of the nutrient-poor surface ocean are dominated numerically by cyanobacteria (Prochlorococcus), cyanobacterial viruses (cyanophage), and alphaproteobacteria (SAR11). How these groups thrive in the diverse physicochemical environments of different oceanic regions remains poorly understood. Comparative metagenomics can reveal adaptive responses linked to ecosystem-specific selective pressures. The Red Sea is well-suited for studying adaptation of pelagic-microbes, with salinities, temperatures, and light levels at the extreme end for the surface ocean, and low nutrient concentrations, yet no metagenomic studies have been done there. The Red Sea (high salinity, high light, low N and P) compares favorably with the Mediterranean Sea (high salinity, low P), Sargasso Sea (low P), and North Pacific Subtropical Gyre (high light, low N). We quantified the relative abundance of genetic functions among Prochlorococcus, cyanophage, and SAR11 from these four regions. Gene frequencies indicate selection for phosphorus acquisition (Mediterranean/Sargasso), DNA repair and high-light responses (Red Sea/Pacific Prochlorococcus), and osmolyte C1 oxidation (Red Sea/Mediterranean SAR11). The unexpected connection between salinity-dependent osmolyte production and SAR11 C1 metabolism represents a potentially major coevolutionary adaptation and biogeochemical flux. Among Prochlorococcus and cyanophage, genes enriched in specific environments had ecotype distributions similar to nonenriched genes, suggesting that inter-ecotype gene transfer is not a major source of environment-specific adaptation. Clustering of metagenomes using gene frequencies shows similarities in populations (Red Sea with Pacific, Mediterranean with Sargasso) that belie their geographic distances. Taken together, the genetic functions enriched in specific environments indicate competitive strategies for maintaining carrying capacity in the face of physical stressors and low nutrient availability.

Monitoring field susceptibility to imidacloprid in the cat flea: a world-first initiative twelve years on.

In 2001, an international surveillance initiative was established, utilising a validated larval development inhibition assay to track the susceptibility of cat flea isolates to imidacloprid. In 2009, an Australian node was incorporated into the programme, joining laboratories in the United States and Europe. Field isolates of Ctenocephalides felis eggs were submitted to participating laboratories and, where egg quantity and quality was sufficient, were placed in the imidacloprid discriminating dose bioassay for evaluation. Between 2002 and 2012, a total of 2,307 cat flea isolates were received across all sites; 1,685 submissions (73 %) were suitable for placement into the bioassay. In the Northern Hemisphere, isolate submission rate was influenced by season, with highest numbers submitted between June and October. In Australia, pets with flea infestations could be sourced year-round, and submission rate was largely influenced by programme factors and not climate. A total of 1,367 valid assays were performed between 2002 and 2012 (assay validity data was not recorded in 2001); adult flea emergence 5 % or greater at 3 ppm imidacloprid was observed in 38 of these assays (2.8 %). For these isolates that reached the threshold for further investigation, re-conduct of the assay using either a repeat challenge dose of 3 ppm of imidacloprid or a dose response probit analysis confirmed their susceptibility to imidacloprid. From 2009 to 2012, the Australian node performed valid assays on 97 field isolates from a total of 136 submissions, with no adult emergence observed at the 3-ppm imidacloprid discriminating dose. In addition to reviewing the data generated by this twelve-year initiative, this paper discusses lessons learned from the coordination and evolution of a complex project across geographically dispersed laboratories on three continents.

Fitting nonstationary general-time-reversible models to obtain edge-lengths and frequencies for the barry-hartigan model.

Among models of nucleotide evolution, the Barry and Hartigan (BH) model (also known as the General Markov Model) is very flexible as it allows separate arbitrary substitution matrices along edges. For a given tree, the estimates of the BH model are a set of joint probability matrices, each giving the pairwise frequencies of nucleotides at the ends of the edge. We have previously shown that, due to an identifiability problem, these cannot be expected to consistently estimate the actual pairwise frequencies. A further consequence is that internal node frequency estimates are likely to be incorrect. Here we define a nonstationary GTR model for each edge that we refer to as the NSGTR model. We fit the NSGTR model by minimizing the sums of squares between the estimates of transition probabilities under the NSGTR model and the estimates provided by a fitted BH model. This NSGTR model provides estimates that avoid the identifiability difficulties of the BH model while closely fitting it. With the best-fitting NSGTR estimates, we are able to get interpretable frequency vectors at internal nodes as well as edge length estimates that are otherwise not yielded by the BH model. These edge lengths are interpretable as the expected number of substitutions along an edge for the model. We also show that for a nonstationary continuous-time model these are not the same as the edge length parameters for conventional substitution matrices that are output by nonstationary model phylogenetic estimation programs such as nhPhyML.

Phylogenetic analysis based on spectral methods.

Whole-genome or multiple gene phylogenetic analysis is of interest since single gene analysis often results in poorly resolved trees. Here, the use of spectral techniques for analyzing multigene data sets is explored. The protein sequences are treated as categorical time series, and a measure of similarity between a pair of sequences, the spectral covariance, is based on the common periodicity between these two sequences. Unlike the other methods, the spectral covariance method focuses on the relationship between the sites of genetic sequences. By properly scaling the dissimilarity measures derived from different genes between a pair of species, we can use the mean of these scaled dissimilarity measures as a summary statistic to measure the taxonomic distances across multiple genes. The methods are applied to three different data sets, one noncontroversial and two with some dispute over the correct placement of the taxa in the tree. Trees are constructed using two distance-based methods, BIONJ and FITCH. A variation of block bootstrap sampling method is used for inference. The methods are able to recover all major clades in the corresponding reference trees with moderate to high bootstrap support. Through simulations, we show that the covariance-based methods effectively capture phylogenetic signal even when structural information is not fully retained. Comparisons of simulation results with the bootstrap permutation results indicate that the covariance-based methods are fairly robust under perturbations in sequence similarity but more sensitive to perturbations in structural similarity.

Pattern classification of phylogeny signals.

In this paper we propose the minimum entropy clustering (MEC) method for clustering genes based on their phylogenetic signals. This entropy based method will cluster two genes together when their concatenation can decrease the entropy. An integral feature of MEC is that it chooses the number of clusters automatically, which is a major advantage over the other methods. Our simulation results show that this method is quite successful in clustering genes with a common phylogeny.

Examining protein structure and similarities by spectral analysis technique.

The spectral envelope, a frequency based technique for analyzing categorical time series, is applied to amino acid sequences to examine their periodicity. The periodic signatures of such sequences is related to the secondary structure of the folding patterns in the gene. For a pair of sequences, we define a spectral envelope covariance which emphasizes the common periodicities in the two sequences. This is used to give a similarity measure for the two sequences which can then be used in a neighbour joining algorithm to construct a phylogeny. We apply the spectral methods to myoglobin sequences from primates and cetaceans. The spectral envelope reflects the structure of this protein and the tree constructed using spectral methods shows strong agreement with published trees. The spectral envelope can be used to explore similarities between and within different protein families. Since we do not require aligned sequences, the spectral methods can be used to create phylogenies across different protein families. We apply the method to 11 protein families from PANDIT obtaining a tree where the families are separated and the relationship among the families is given.

Test a clade in phylogenetic trees.

We develop a new method for testing a portion of a tree (called a clade) based on multiple tests of many 4-taxon trees in this paper. This is particularly useful when the phylogenetic tree constructed by other methods have a clade that is difficult to explain from a biological point of view. The statement about the test of the clade can be made through the multiple P values from these individual tests. By controlling the familywise error rate or the false discovery rate (FDR), 4 different tree test methods are evaluated through simulation methods. It shows that the combination of the approximately unbiased (AU) test and the FDR-controlling procedure provides strong power along with reasonable type I error rate and less heavy computation.

The comparison of the confidence regions in phylogeny.

In this paper, several different procedures for constructing confidence regions for the true evolutionary tree are evaluated both in terms of coverage and size without considering model misspecification. The regions are constructed on the basis of tests of hypothesis using six existing tests: Shimodaira Hasegawa (SH), SOWH, star form of SOWH (SSOWH), approximately unbiased (AU), likelihood weight (LW), generalized least squares, plus two new tests proposed in this paper: single distribution nonparametric bootstrap (SDNB) and single distribution parametric bootstrap (SDPB). The procedures are evaluated on simulated trees both with small and large number of taxa. Overall, the SH, SSOWH, AU, and LW tests led to regions with higher coverage than the nominal level at the price of including large numbers of trees. Under the specified model, the SOWH test gives accurate coverage and relatively small regions. The SDNB and SDPB tests led to the small regions with occasional undercoverage. These two procedures have a substantial computational advantage over the SOWH test. Finally, the cutoff levels for the SDNB test are shown to be more variable than those for the SDPB test.

Estimation of rates-across-sites distributions in phylogenetic substitution models.

Previous work has shown that it is often essential to account for the variation in rates at different sites in phylogenetic models in order to avoid phylogenetic artifacts such as long branch attraction. In most current models, the gamma distribution is used for the rates-across-sites distributions and is implemented as an equal-probability discrete gamma. In this article, we introduce discrete distribution estimates with large numbers of equally spaced rate categories allowing us to investigate the appropriateness of the gamma model. With large numbers of rate categories, these discrete estimates are flexible enough to approximate the shape of almost any distribution. Likelihood ratio statistical tests and a nonparametric bootstrap confidence-bound estimation procedure based on the discrete estimates are presented that can be used to test the fit of a parametric family. We applied the methodology to several different protein data sets, and found that although the gamma model often provides a good parametric model for this type of data, rate estimates from an equal-probability discrete gamma model with a small number of categories will tend to underestimate the largest rates. In cases when the gamma model assumption is in doubt, rate estimates coming from the discrete rate distribution estimate with a large number of rate categories provide a robust alternative to gamma estimates. An alternative implementation of the gamma distribution is proposed that, for equal numbers of rate categories, is computationally more efficient during optimization than the standard gamma implementation and can provide more accurate estimates of site rates.

Laryngoscopic intubation: learning and performance.

BACKGROUND: Many healthcare professionals are trained in direct laryngoscopic tracheal intubation (LEI), which is a potentially lifesaving procedure. This study attempts to determine the number of successful LEI exposures required during training to assure competent performance, with special emphasis on defining competence itself. METHODS: Analyses were based on a longitudinal study of novices under training conditions in the operating room. The progress of 438 LEIs performed by the 20 nonanesthesia trainees was monitored by observation and videotape analysis. Eighteen additional LEIs were performed by experienced anesthesiologists to define the standard. A generalized linear, mixed-modelling approach was used to identify key aspects of effective training and performance. The number of tracheal intubations that the trainees were required to perform before acquiring expertise in LEI was estimated. RESULTS: Subjects performed between 18 and 35 laryngoscopic intubations. However, statistical modeling indicates that a 90% probability of a "good intubation" required 47 attempts. Proper insertion and lifting of the laryngoscope were crucial to "good" or "competent" performance of LEI. Traditional features, such as proper head and neck positions, were found to be less important under the study conditions. CONCLUSIONS: This study determined that traditional LEI teaching for nonanesthesia personnel using manikin alone is inadequate. A reevaluation of current standards in LEI teaching for nonanesthesia is required.

Testing for differences in rates-across-sites distributions in phylogenetic subtrees.

It has long been recognized that the rates of molecular evolution vary amongst sites in proteins. The usual model for rate heterogeneity assumes independent rate variation according to a rate distribution. In such models the rate at a site, although random, is assumed fixed throughout the evolutionary tree. Recent work by several groups has suggested that rates at sites often vary across subtrees of the larger tree as well as across sites. This phenomenon is not captured by most phylogenetic models but instead is more similar to the covarion model of Fitch and coworkers. In this article we present methods that can be useful in detecting whether different rates occur in two different subtrees of the larger tree and where these differences occur. Parametric bootstrapping and orthogonal regression methodologies are used to test for rate differences and to make statements about the general differences in the rates at sites. Confidence intervals based on the conditional distributions of rates at sites are then used to detect where the rate differences occur. Such methods will be helpful in studying the phylogenetic, structural, and functional bases of changes in evolutionary rates at sites, a phenomenon that has important consequences for deep phylogenetic inference.