Litter decomposition across multiple spatial scales in stream networks

Spatial scale is a critical consideration for understanding ecological patterns and controls of ecological processes, yet very little is known about how rates of fundamental ecosystem processes vary across spatial scales. We assessed litter decomposition in stream networks whose inherent hierarchical nature makes them a suitable model system to evaluate variation in decay rates across multiple spatial scales. Our hypotheses were (1) that increasing spatial extent adds significant variability at each hierarchical level, and (2) that stream size is an important source of variability among streams. To test these hypotheses we let litter decompose in four riffles in each of twelve 3rd-order streams evenly distributed across four 4th-order watersheds, and in a second experiment determined variation in decomposition rate along a stream-size gradient ranging from orders 1 to 4. Differences in decay rates between coarse-mesh and fine-mesh litter bags accounted for much of the overall variability in the data sets, and were remarkably consistent across spatial scales and stream sizes. In particular, variation across watersheds was minor. Differences among streams and among riffles were statistically significant, though relatively small, leaving most of the total variance (51%) statistically unexplained. This result suggests that variability was generated mainly within riffles, decreasing successively with increasing scale. A broad range of physical and chemical attributes measured at the study sites explained little of the variance in decomposition rate. This, together with the strong mesh-size effect and greater variability among coarse-mesh bags, suggests that detritivores account, at least partly, for the unexplained variance. These findings contrast with the widespread perception that variability of ecosystem characteristics, including process rates, invariably increases (1) with spatial extent and (2), in stream networks, when analyses encompass headwaters of various size. An important practical implication is that natural variability need not compromise litter decomposition assays as a means of assessing functional ecosystem integrity. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Discrimination and scoring using small sets of genes for two-sample microarray data

Comparison of gene expression for two groups of individuals form an important subclass of microarray experiments. We study multivariate procedures, in particular use of Hotelling's T2 for discrimination between the groups with a special emphasis on methods based on few genes only. We apply the methods to data from an experiment with a group of atopic dermatitis patients compared with a control group. We also compare our methodology to other recently proposed methods on publicly available datasets. It is found that (i) use of several genes gives a much improved discrimination of the groups as compared to one gene only, (ii) the genes that play the most important role in the multivariate analysis are not necessarily those that rank first in univariate comparisons of the groups, (iii) Linear Discriminant Analysis carried out with sets of 2-5 genes selected according to their Hotelling T2 give results comparable to state-of-the-art methods using many more genes, a feature of our method which might be crucial in clinical applications. Finding groups of genes that together give optimal multivariate discrimination (given the size of the group) can identify crucial pathways and networks of genes responsible for a disease. The computer code that we developed to make computations is available as an R package.     

Impact of scoring error and reproducibility RAPD data on RAPD based estimates of genetic distance

RAPD band reproducibility and scoring error were evaluated for RAPDs generated by 50 RAPD primers among ten snap bean (Phaseolus vulgaris L.) genotypes. Genetic distances based on different sets of RAPD bands were compared to evaluate the impact of scoring error, reproducibility, and differences in relative amplification strength on the reproducibility of RAPD based genetic distance estimates. The measured RAPD data scoring error was 2%. Reproducibility, expressed as the percentage of RAPD bands scored that are also scored in replicate data, was 76%. The results indicate that the probability of a scored RAPD band being scored in replicate data is strongly dependent on the uniformity of amplification conditions between experiments, as well as the relative amplification strength of the RAPD band. Significant improvement in the reproducibility of scored bands and some reduction in scoring error was achieved by reducing differences in reaction conditions between replicates. Observed primer variability for the reproducibility of scored RAPDs may also facilitate the selection of primers, resulting in dramatic improvements in the reproducibility of RAPD data used in germplasm studies. Variance of genetic distances across replicates due to sampling error was found to be more than six times greater than that due to scoring error for a set of 192 RAPD bands. Genetic distance matrices computed from the RAPD bands scored in replicated data and RAPD bands that failed to be scored in replicated data were not significantly different. Differences in the ethidium bromide staining intensity of RAPD bands were not associated with significant differences in resulting genetic distance matrices. The assumption of sampling error as the only source of error was sufficient to account for the observed variation in genetic distance estimates across independent sets of RAPD bands.     

Bayesian meta-analysis models for microarray data: a comparative study

Background

With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods.

Results

Two Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce the gene-specific posterior probability of differential expression, which is the basis for inference. Since the standardized expression integration model includes inter-study variability, it may improve accuracy of results versus the probability integration model. However, due to the small number of studies typical in microarray meta-analyses, the variability between studies is challenging to estimate. The probability integration model eliminates the need to model variability between studies, and thus its implementation is more straightforward. We found in simulations of two and five studies that combining probabilities outperformed combining standardized gene expression measures for three comparison values: the percent of true discovered genes in meta-analysis versus individual studies; the percent of true genes omitted in meta-analysis versus separate studies, and the number of true discovered genes for fixed levels of Bayesian false discovery. We identified similar results when pooling two independent studies of Bacillus subtilis. We assumed that each study was produced from the same microarray platform with only two conditions: a treatment and control, and that the data sets were pre-scaled.

Conclusion

The Bayesian meta-analysis model that combines probabilities across studies does not aggregate gene expression measures, thus an inter-study variability parameter is not included in the model. This results in a simpler modeling approach than aggregating expression measures, which accounts for variability across studies. The probability integration model identified more true discovered genes and fewer true omitted genes than combining expression measures, for our data sets.     

Measuring temporal patterns in ecology: The case of mast seeding

1. Properly assessing temporal patterns is a central issue in ecology in order to understand ecosystem processes and their mechanisms. Mast seeding has traditionally been described as a reproductive behavior consisting of highly variable and synchronized reproductive events. The most common metric used to measure temporal variability and thus infer masting behavior, the coefficient of variation (CV), however, has been repeatedly suggested to improperly estimate temporal variability. Biases of CV estimates are especially problematic for non‐normally distributed data and/or data sets with a high number of zeros.
2. Some recent studies have already adopted new metrics to measure temporal variability, but most continue to use CV. This controversy has started a strong debate about what metrics to use.
3. We here summarize the problems of CV when assessing temporal variability, particularly across data sets containing a large number of zeros, and highlight the benefits of using other metrics of temporal variability, such as proportional variability (PV) and consecutive disparity (D). We also suggest a new way to look at reproductive behavior, by separating temporal variability from frequency of reproduction, to allow better comparison of data sets with different characteristics.
4. We suggest future studies to properly describe the temporal patterns in fully scientific and measurable terms that do not lead to confusion, such as variability and frequency of reproduction, using robust and fully comparable metrics.

     

Vagal-dependent nonlinear variability in the respiratory pattern of anesthetized, spontaneously breathing rats

Physiological rhythms, including respiration, exhibit endogenous variability associated with health, and deviations from this are associated with disease. Specific changes in the linear and nonlinear sources of breathing variability have not been investigated. In this study, we used information theory-based techniques, combined with surrogate data testing, to quantify and characterize the vagal-dependent nonlinear pattern variability in urethane-anesthetized, spontaneously breathing adult rats. Surrogate data sets preserved the amplitude distribution and linear correlations of the original data set, but nonlinear correlation structure in the data was removed. Differences in mutual information and sample entropy between original and surrogate data sets indicated the presence of deterministic nonlinear or stochastic non-Gaussian variability. With vagi intact (n = 11), the respiratory cycle exhibited significant nonlinear behavior in templates of points separated by time delays ranging from one sample to one cycle length. After vagotomy (n = 6), even though nonlinear variability was reduced significantly, nonlinear properties were still evident at various time delays. Nonlinear deterministic variability did not change further after subsequent bilateral microinjection of MK-801, an N-methyl-D-aspartate receptor antagonist, in the K?lliker-Fuse nuclei. Reversing the sequence (n = 5), blocking N-methyl-D-aspartate receptors bilaterally in the dorsolateral pons significantly decreased nonlinear variability in the respiratory pattern, even with the vagi intact, and subsequent vagotomy did not change nonlinear variability. Thus both vagal and dorsolateral pontine influences contribute to nonlinear respiratory pattern variability. Furthermore, breathing dynamics of the intact system are mutually dependent on vagal and pontine sources of nonlinear complexity. Understanding the structure and modulation of variability provides insight into disease effects on respiratory patterning.     

Physiological and anatomical trait variability of dominant C4 grasses

Climate variability is a key driver of physiological responses in common grass species in grasslands of North America. Differences in microanatomical traits among coexisting species may influence physiological responses to climate variability over large geographic scales. The goal of this research was to determine leaf-level physiological and microanatomical trait variability among four dominant C₄ grass species across a natural precipitation gradient. Physiological traits were observed to vary significantly across the gradient with greater variability than microanatomical traits. Microanatomical traits were shown to predict physiological responses in A. gerardii and P. virgatum, but the nature of the relationships varied between species. These results illustrate that microanatomical and physiological traits vary across a precipitation gradient, there are clear linkages between microanatomy and physiology in grass species, and this evidence underscores the need for further investigation using phylogenetically diverse assemblages.     

Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type

Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long‐term stable habitats. The variability of complex, short‐term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs’ usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and flower availability. Additionally, testing SDMs with field surveys should involve multiple collection techniques.     

A general approach to simultaneous model fitting and variable elimination in response models for biological data with many more variables than observations

Background  

With the advent of high throughput biotechnology data acquisition platforms such as micro arrays, SNP chips and mass spectrometers, data sets with many more variables than observations are now routinely being collected. Finding relationships between response variables of interest and variables in such data sets is an important problem akin to finding needles in a haystack. Whilst methods for a number of response types have been developed a general approach has been lacking.     

Retrospective analysis of the relationships among the test environments of the Southern Queensland sugarcane breeding programme

Repeatability of aspects of genotype by environment (GxE) interactions is an important factor to be assessed in designing more efficient selection programmes. Sugar yield data from multi environment trials (METs) which were part of the sugarcane breeding programme in southern Queensland were analysed. Data were obtained from 71 environments consisting of trials planted from 1986 to 1989. Retrospective analysis on these data was conducted to assess the repeatability of the clone by environment (CxE) interactions over locations and years. This analysis focussed on identifying similarities among test environments in the way they discriminated among clones for sugar yield. Analyses of variance and pattern analyses on environments over years based on standardised data were conducted. The pattern analyses were done sequentially according to the accumulated data sets over years. Squared Euclidean distances among environments were averaged over data sets and years before pattern analyses across the data sets were conducted. A graphical methodology was developed to present the results of the cumulative historical analysis. CxE interactions of a magnitude which affected selection decisions were present in each data set studied. Pattern analyses on cumulative data sets identified environmental groupings that were based on geographical positions. Each location generated a different pattern of discrimination among the clones. These results emphasised the importance of clone by location (CxL) interactions in southern Queensland and the need to concentrate more on testing across locations than on ratooning ability within a location. The classifications identified similarities among ratoon crops within a location, differences among locations and differences between ratoon crops and their plant crop (PC). This suggested that some aspects of CxL and clone by crop-year (CxY) interactions were repeatable across years. The potential applications of these results to increase efficiency of the sugarcane breeding programme, such as the possibility of applying indirect selection among environments generating similar discrimination among clones, are discussed.Abbreviations GxE Genotype-by-environment interactions - METs multi-environment trials - CxE clone-by-en-vironment interactions - CxL clone-by-location interactions - PC plant crop - CxY clone-by-crop-year interactions - CxLxF clone-by-location-by-crop-year interactions - SYT substation yield trials - BSES Bureau of SugarExperiment Stations     

Beta diversity and taxonomic sufficiency: Do higher‐level taxa reflect heterogeneity in species composition?

Aim Virtually all studies exploring the use of taxonomic surrogates in assessing patterns of diversity have focused on clear shifts in the location of samples in multivariate space. The potential use of coarser levels of taxonomic resolution to detect patterns of variability in multivariate space, corresponding to β‐diversity in the case of presence/absence data, remains unexplored. Here we considered five ecological data sets of highly diverse marine molluscan assemblages to test the hypothesis that patterns in compositional heterogeneity would be maintained at coarser levels of taxonomic resolution. Location Italy, Norway, New Zealand and the Arctic. Methods We used multivariate dispersion based on the Jaccard resemblance measure of presence/absence data as a measure of β‐diversity to test the null hypothesis that patterns of heterogeneity in species composition for molluscs would be maintained at coarser levels of taxonomic resolution. Tests to compare β‐diversities among groups (based on distances to centroids and using 9999 permutations) were carried out separately for each of five data sets at the species level and then for each of genus, family, order and class levels. Results Differences in multivariate dispersion at the species level (heterogeneity in the identities of species) were maintained for genera and for families, but not at coarser levels of taxonomic resolution (order or class). These results were consistent across all data sets, despite differences in their spatial scale and extent, geographical location, environmental and habitat features (benthic soft sediments, rocky reefs or kelp holdfasts). Main conclusions These results suggest that either genera or families may be used as effective taxonomic surrogates to detect spatial differences in β‐diversity for molluscs. The use of surrogates can provide considerable sampling efficiencies for biodiversity assessments. We consider, however, that a degree of caution and more work is needed, as heterogeneity at the species level may not be reflected by taxonomic surrogates at smaller spatial scales.     

Discrimination measures for survival outcomes: connection between the AUC and the predictiveness curve

Finding out biomarkers and building risk scores to predict the occurrence of survival outcomes is a major concern of clinical epidemiology, and so is the evaluation of prognostic models. In this paper, we are concerned with the estimation of the time-dependent AUC--area under the receiver-operating curve--which naturally extends standard AUC to the setting of survival outcomes and enables to evaluate the discriminative power of prognostic models. We establish a simple and useful relation between the predictiveness curve and the time-dependent AUC--AUC(t). This relation confirms that the predictiveness curve is the key concept for evaluating calibration and discrimination of prognostic models. It also highlights that accurate estimates of the conditional absolute risk function should yield accurate estimates for AUC(t). From this observation, we derive several estimators for AUC(t) relying on distinct estimators of the conditional absolute risk function. An empirical study was conducted to compare our estimators with the existing ones and assess the effect of model misspecification--when estimating the conditional absolute risk function--on the AUC(t) estimation. We further illustrate the methodology on the Mayo PBC and the VA lung cancer data sets.     

Ice ages leave genetic diversity ‘hotspots’ in Europe but not in Eastern North America

After the last glacial cycle, temperate European trees migrated northward, experiencing genetic bottlenecks and founder effects, which left high haplotype endemism in southern populations and clines in genetic diversity northward. These patterns are thought to be ubiquitous across temperate forests, and are therefore used to anticipate the potential genetic consequences of future warming. We compared existing and new phylogeographic data sets (chloroplast DNA) from 14 woody taxa in Eastern North America (ENA) to data sets from 21 ecologically similar European species to test for common impacts of Quaternary climate swings on genetic diversity across diverse taxa and between continents. Unlike their European counterparts, ENA taxa do not share common southern centres of haplotype endemism and they generally maintain high genetic diversity even at their northern range limits. Differences between the genetic impacts of Quaternary climate cycles across continents suggest refined lessons for managing genetic diversity in today's warming world.     

Testing the suitability of a morphological monitoring approach for identifying temporal variability in a temperate sponge assemblage

Sponges are a dominant component of benthic assemblages in hard substratum environments across the world, but despite the importance of sponges, they are generally poorly represented in most marine monitoring programmes. There is considerable need to develop effective monitoring tools to monitor changes in sponge assemblages. Morphological monitoring has been proposed as a suitable method to monitor sponges and while morphological monitoring has already taken place at Skomer Marine Reserve (MNR), Wales, here we investigate whether species level and morphological level data sets are correlated with respect to temporal variation. Furthermore, we examine the environmental factors that correlate with the patterns of temporal variability. Both species and morphological data sets revealed significant seasonal changes and spatial variation in sponge assemblages; these data sets were highly correlated and explained by a number of environmental factors. We conclude that morphological monitoring of sponge assemblages may represents a cost-effective method for assessing temporal and spatial variation in sponges, where full species level monitoring is not possible, as patterns identified from morphological data were a suitable surrogate of species-level data.     

The influence of estimated body segment parameters on predicted joint kinetics during diplegic cerebral palsy gait

Inverse Dynamic calculations are routinely used in joint moment and power estimates during gait with anthropometric data often taken from published sources. Many biomechanical analyses have highlighted the need to obtain subject-specific anthropometric data (e.g. Mass, Centre of Mass, Moments of Inertia) yet the types of imaging techniques required to achieve this are not always available in the clinical setting. Differences in anthropometric sets have been shown to affect the reactive force and moment calculations in normal subjects but the effect on a paediatric diplegic cerebral palsy group has not been investigated. The aim of this study was to investigate the effect of using different anthropometric sets on predicted sagittal plane moments during normal and diplegic cerebral palsy gait. Three published anthropometric sets were applied to the reactive force and moment calculations of 14 Cerebral Palsy and 14 Control subjects. Statistically significant differences were found when comparing the different anthropometric sets but variability in the resulting sagittal plane moment calculations between sets was low (0.01–0.07 Nm/kg). In addition, the GDI-Kinetic, used as an outcome variable to assess whether differences were clinically meaningful, indicated no clinically meaningful difference between sets. The results suggest that the effects of using different anthropometric sets on the kinetic profiles of normal and diplegic cerebral palsy subjects are clinically insignificant.     

From information to understanding: the role of model organism databases in comparative and functional genomics

Data integration is key to functional and comparative genomics because integration allows diverse data types to be evaluated in new contexts. To achieve data integration in a scalable and sensible way, semantic standards are needed, both for naming things (standardized nomenclatures, use of key words) and also for knowledge representation. The Mouse Genome Informatics database and other model organism databases help to close the gap between information and understanding of biological processes because these resources enforce well-defined nomenclature and knowledge representation standards. Model organism databases have a critical role to play in ensuring that diverse kinds of data, especially genome-scale data sets and information, remain useful to the biological community in the long-term. The efforts of model organism database groups ensure not only that organism-specific data are integrated, curated and accessible but also that the information is structured in such a way that comparison of biological knowledge across model organisms is facilitated.     

Comparison of statistical methods commonly used in predictive modelling

Logistic Multiple Regression, Principal Component Regression and Classification and Regression Tree Analysis (CART), commonly used in ecological modelling using GIS, are compared with a relatively new statistical technique, Multivariate Adaptive Regression Splines (MARS), to test their accuracy, reliability, implementation within GIS and ease of use. All were applied to the same two data sets, covering a wide range of conditions common in predictive modelling, namely geographical range, scale, nature of the predictors and sampling method. We ran two series of analyses to verify if model validation by an independent data set was required or cross‐validation on a learning data set sufficed. Results show that validation by independent data sets is needed. Model accuracy was evaluated using the area under Receiver Operating Characteristics curve (AUC). This measure was used because it summarizes performance across all possible thresholds, and is independent of balance between classes. MARS and Regression Tree Analysis achieved the best prediction success, although the CART model was difficult to use for cartographic purposes due to the high model complexity.     

FAMCS： Finding All Maximal Common Substructures in Proteins

Finding the common substructures shared by two proteins is considered as one of the central issues in computational biology because of its usefulness in understanding the structure-function relationship and application in drug and vaccine design. In this paper, we propose a novel algorithm called FAMCS （Finding All Maximal Common Substructures） for the common substructure identification problem. Our method works initially at the protein secondary structural element （SSE） level and starts with the identification of all structurally similar SSE pairs. These SSE pairs are then merged into sets using a modified Apriori algorithm, which will test the similarity of various sets of SSE pairs incrementally until all the maximal sets of SSE pairs that deemed to be similar are found. The maximal common substructures of the two proteins will be formed from these maximal sets. A refinement algorithm is also proposed to fine tune the alignment from the SSE level to the residue level. Comparison of FAMCS with other methods on various proteins shows that FAMCS can address all four requirements and infer interesting biological discoveries.     

A theory for the use of visual orientation information which exploits the columnar structure of striate cortex

A neural model is constructed based on the structure of a visual orientation hypercolumn in mammalian striate cortex. It is then assumed that the perceived orientation of visual contours is determined by the pattern of neuronal activity across orientation columns. Using statistical estimation theory, limits on the precision of orientation estimation and discrimination are calculated. These limits are functions of single unit response properties such as orientation tuning width, response amplitude and response variability, as well as the degree of organization in the neural network. It is shown that a network of modest size, consisting of broadly orientation selective units, can reliably discriminate orientation with a precision equivalent to human performance. Of the various network parameters, the discrimination threshold depends most critically on the number of cells in the hypercolumn. The form of the dependence on cell number correctly predicts the results of psychophysical studies of orientation discrimination. The model system's performance is also consistent with psychophysical data in two situations in which human performance is not optimal. First, interference with orientation discrimination occurs when multiple stimuli activate cells in the same hypercolumn. Second, systematic errors in the estimation of orientation can occur when a stimulus is composed of intersecting lines. The results demonstrate that it is possible to relate neural activity to visual performance by an examination of the pattern of activity across orientation columns. This provides support for the hypothesis that perceived orientation is determined by the distributed pattern of neural activity. The results also encourage the view of neural activity. The results also are determined by the responses of many neurons rather than the sensitivity of individual cells.