A metacommunity framework for enhancing the effectiveness of biological monitoring strategies

Because of inadequate knowledge and funding, the use of biodiversity indicators is often suggested as a way to support management decisions. Consequently, many studies have analyzed the performance of certain groups as indicator taxa. However, in addition to knowing whether certain groups can adequately represent the biodiversity as a whole, we must also know whether they show similar responses to the main structuring processes affecting biodiversity. Here we present an application of the metacommunity framework for evaluating the effectiveness of biodiversity indicators. Although the metacommunity framework has contributed to a better understanding of biodiversity patterns, there is still limited discussion about its implications for conservation and biomonitoring. We evaluated the effectiveness of indicator taxa in representing spatial variation in macroinvertebrate community composition in Atlantic Forest streams, and the processes that drive this variation. We focused on analyzing whether some groups conform to environmental processes and other groups are more influenced by spatial processes, and on how this can help in deciding which indicator group or groups should be used. We showed that a relatively small subset of taxa from the metacommunity would represent 80% of the variation in community composition shown by the entire metacommunity. Moreover, this subset does not have to be composed of predetermined taxonomic groups, but rather can be defined based on random subsets. We also found that some random subsets composed of a small number of genera performed better in responding to major environmental gradients. There were also random subsets that seemed to be affected by spatial processes, which could indicate important historical processes. We were able to integrate in the same theoretical and practical framework, the selection of biodiversity surrogates, indicators of environmental conditions, and more importantly, an explicit integration of environmental and spatial processes into the selection approach.     

metacom: an R package for the analysis of metacommunity structure

Metacommunity theory is an extension of metapopulation theory with the goal of understanding how ecological communities vary through space and time. One off‐shoot of metacommunity theory deals with understanding how community structure varies along biotic or environmental gradients. The Elements of Metacommunity Structure framework is a three‐tiered analysis of metacommunity structure that enables the user to identify metacommunity properties that arise in site‐by‐species incidence matrices. These properties can then be related to underlying variables that influence species distributions. The EMS framework is now implemented in metacom, an open source R package that allows for the analysis and plotting of metacommunities.     

A mathematical framework for protein structure comparison

Comparison of protein structures is important for revealing the evolutionary relationship among proteins, predicting protein functions and predicting protein structures. Many methods have been developed in the past to align two or multiple protein structures. Despite the importance of this problem, rigorous mathematical or statistical frameworks have seldom been pursued for general protein structure comparison. One notable issue in this field is that with many different distances used to measure the similarity between protein structures, none of them are proper distances when protein structures of different sequences are compared. Statistical approaches based on those non-proper distances or similarity scores as random variables are thus not mathematically rigorous. In this work, we develop a mathematical framework for protein structure comparison by treating protein structures as three-dimensional curves. Using an elastic Riemannian metric on spaces of curves, geodesic distance, a proper distance on spaces of curves, can be computed for any two protein structures. In this framework, protein structures can be treated as random variables on the shape manifold, and means and covariance can be computed for populations of protein structures. Furthermore, these moments can be used to build Gaussian-type probability distributions of protein structures for use in hypothesis testing. The covariance of a population of protein structures can reveal the population-specific variations and be helpful in improving structure classification. With curves representing protein structures, the matching is performed using elastic shape analysis of curves, which can effectively model conformational changes and insertions/deletions. We show that our method performs comparably with commonly used methods in protein structure classification on a large manually annotated data set.     

A framework for clinical evaluation of diagnostic technologies.

Most new diagnostic technologies have not been adequately assessed to determine whether their application improves health. Comprehensive evaluation of diagnostic technologies includes establishing technologic capability and determining the range of possible uses, diagnostic accuracy, impact on the health care provider, therapeutic impact and impact on patient outcome. Guidelines to determine whether each of these criteria have been met adequately are presented. Diagnostic technologies should be disseminated only if they are less expensive, produce fewer untoward effects and are at least as accurate as existing methods, if they eliminate the need for other investigations without loss of accuracy, or if they lead to institution of effective therapy. Establishing patient benefit often requires a randomized controlled trial in which patients receive the new test or an alternative diagnostic strategy. Other study designs are logistically less difficult but may not provide accurate assessment of benefit. Rigorous assessment of diagnostic technologies is needed for efficient use of health care resources.     

Hydrological connectivity determining metacommunity structure of planktonic heterotrophic flagellates

Dispersal potential of organisms is directly related to the hydrological connectivity among habitats. We investigated if the relative role of the environmental and spatial components in structuring the heterotrophic flagellates depends on the degree of hydrological connectivity. Samplings were performed in aquatic environments of the Paraná River floodplain, which differ in their degree of connectivity: lotic environments, connected lakes and isolated lakes (temporarily connected to the main river only during floods). We expect that communities in isolated lakes would be more subject to dispersal limitation, while in connected lakes and lotic environments the communities would be regulated mainly by environmental variables (species sorting). We sampled in the pelagic region of 23 environments during the low water period in 2014. The results of pRDA revealed that the contributions of environmental and spatial factors differed between the types of environments. The greater contribution of the environmental variables in structuring the flagellates metacommunity, regardless of the type of environment, may be related to the elevated dispersal capacity of microorganisms. The spatial component was also significant in the isolated lakes. Our results support the idea that microorganism communities are mainly structured by environmental factors, although the spatial component seems important when lakes are isolated.     

Beyond connectivity: how the structure of dispersal influences metacommunity dynamics

Dispersal within metacommunities can play a major role in species persistence by promoting asynchrony between communities. Understanding this role is crucial both for explaining species coexistence and managing landscapes that are increasingly fragmented by human activities. Here, we demonstrate that spatial patterning of dispersal connections can drastically alter both the tendency toward asynchrony and the effect of asynchrony on metacommunity dynamics commonly used to infer the potential for persistence. We also demonstrate that changes in dispersal connections in strictly homogeneous predator-prey metacommunities can generate an extremely rich variety of dynamics even when previously investigated properties of connectivity such as the magnitude and distribution of dispersal among patches are held constant. Furthermore, the dynamics we observe depend strongly on initial conditions. Our results illustrate the effectiveness of measures of spatial structure for predicting asynchrony and its effects on community dynamics, providing a deeper understanding of the relationship between spatial structure and species persistence in metacommunities.     

Predicting food‐web structure with metacommunity models

Synthesis Metacommunity theory aims to elucidate the relative influence of local and regional‐scale processes in generating diversity patterns across the landscape. Metacommunity research has focused largely on assemblages of competing organisms within a single trophic level. Here, we test the ability of metacommunity models to predict the network structure of the aquatic food web found in the leaves of the northern pitcher plant Sarracenia purpurea. The species‐sorting and patch‐dynamics models most accurately reproduced nine food web properties, suggesting that local‐scale interactions play an important role in structuring Sarracenia food webs. Our approach can be applied to any well‐resolved food web for which data are available from multiple locations. The metacommunity framework explores the relative influence of local and regional‐scale processes in generating diversity patterns across the landscape. Metacommunity models and empirical studies have focused mostly on assemblages of competing organisms within a single trophic level. Studies of multi‐trophic metacommunities are predominantly restricted to simplified trophic motifs and rarely consider entire food webs. We tested the ability of the patch‐dynamics, species‐sorting, mass‐effects, and neutral metacommunity models, as well as three hybrid models, to reproduce empirical patterns of food web structure and composition in the complex aquatic food web found in the northern pitcher plant Sarracenia purpurea. We used empirical data to determine regional species pools and estimate dispersal probabilities, simulated local food‐web dynamics, dispersed species from regional pools into local food webs at rates based on the assumptions of each metacommunity model, and tested their relative fits to empirical data on food‐web structure. The species‐sorting and patch‐dynamics models most accurately reproduced nine food web properties, suggesting that local‐scale interactions were important in structuring Sarracenia food webs. However, differences in dispersal abilities were also important in models that accurately reproduced empirical food web properties. Although the models were tested using pitcher‐plant food webs, the approach we have developed can be applied to any well‐resolved food web for which data are available from multiple locations.     

A comprehensive framework for prioritizing variants in exome sequencing studies of Mendelian diseases

Exome sequencing strategy is promising for finding novel mutations of human monogenic disorders. However, pinpointing the casual mutation in a small number of samples is still a big challenge. Here, we propose a three-level filtration and prioritization framework to identify the casual mutation(s) in exome sequencing studies. This efficient and comprehensive framework successfully narrowed down whole exome variants to very small numbers of candidate variants in the proof-of-concept examples. The proposed framework, implemented in a user-friendly software package, named KGGSeq (http://statgenpro.psychiatry.hku.hk/kggseq), will play a very useful role in exome sequencing-based discovery of human Mendelian disease genes.     

A comprehensive system for evaluation of remote sequence similarity detection

Background  

Accurate and sensitive performance evaluation is crucial for both effective development of better structure prediction methods based on sequence similarity, and for the comparative analysis of existing methods. Up to date, there has been no satisfactory comprehensive evaluation method that (i) is based on a large and statistically unbiased set of proteins with clearly defined relationships; and (ii) covers all performance aspects of sequence-based structure predictors, such as sensitivity and specificity, alignment accuracy and coverage, and structure template quality.     

A comprehensive evaluation of assembly scaffolding tools

Background

Genome assembly is typically a two-stage process: contig assembly followed by the use of paired sequencing reads to join contigs into scaffolds. Scaffolds are usually the focus of reported assembly statistics; longer scaffolds greatly facilitate the use of genome sequences in downstream analyses, and it is appealing to present larger numbers as metrics of assembly performance. However, scaffolds are highly prone to errors, especially when generated using short reads, which can directly result in inflated assembly statistics.

Results

Here we provide the first independent evaluation of scaffolding tools for second-generation sequencing data. We find large variations in the quality of results depending on the tool and dataset used. Even extremely simple test cases of perfect input, constructed to elucidate the behaviour of each algorithm, produced some surprising results. We further dissect the performance of the scaffolders using real and simulated sequencing data derived from the genomes of Staphylococcus aureus, Rhodobacter sphaeroides, Plasmodium falciparum and Homo sapiens. The results from simulated data are of high quality, with several of the tools producing perfect output. However, at least 10% of joins remains unidentified when using real data.

Conclusions

The scaffolders vary in their usability, speed and number of correct and missed joins made between contigs. Results from real data highlight opportunities for further improvements of the tools. Overall, SGA, SOPRA and SSPACE generally outperform the other tools on our datasets. However, the quality of the results is highly dependent on the read mapper and genome complexity.     

A correct formulation for a spatially implicit predator-prey metacommunity model

In order to mitigate the problem of increasing model complexity with increasing number of occupation states in spatially implicit metacommunity models, the assumption of independency among species distributions is often required. In the present paper, we show that this approach only works correctly if set relations among patch occupancy states are considered adequately. This is illustrated by means of a well-known, although incorrectly formulated, predator-prey metacommunity model devised by Bascompte and Solé [1]. We demonstrate that this model shows anomalous dynamical behavior caused by inconsistence between the model formulation and its assumptions. In order to formalize our finding we develop a corrected model formulation that accounts for the principles of set theory so that the sum of the system compartments change rate is nulled. Applying this method successfully rules out the occurrence of anomalous dynamical behavior found in the original model. Finally we discuss the implications of our findings for the accuracy of model predictions.     

Plant metacommunity structure remains unchanged during biodiversity loss in English woodlands

The metacommunity concept provides important insights into large‐scale patterns and dynamics of distributions of interacting species. However, temporal change of metacommunity structure is little studied and has not been previously analysed in the context of biodiversity change. As metacommunity structure is determined by multiple species distributions, it is expected to change as a result of biodiversity loss. To examine this process, we analysed structural change of a southern English woodland metacommunity at two points in time, seven decades apart. During this interval, the metacommunity lost β‐diversity through taxonomic homogenization. We performed an ‘elements of metacommunity structure’ (EMS) analysis to examine metacommunity structure, based upon three structural elements: coherence (i.e. gaps in species range along a structuring gradient), spatial turnover (replacements), and species range boundary clumping. We predicted that metacommunity structure would decrease in spatial turnover and thus become more nested over time. We tested for change in individual structural elements with z‐scores and examined the role of spatial and environmental variables as potential structuring mechanisms through correlation with EMS ordination axes. Our results demonstrated that the metacommunity had a Clementsian structure that was maintained over time. Despite no change in broad structure, coherence and species range boundary clumping increased. Spatial turnover increased along the first structuring gradient but decreased on the second gradient. We hypothesise that this difference between gradients may reflect the presence of competing processes affecting spatial turnover. The mechanisms of biological structuring involved both environmental and spatial factors at the scale of the individual woodland. Therefore, our results suggest that broad metacommunity structure would not be a good landscape‐scale indicator for conservation status. Conversely, knowledge that metacommunity structure does not change over time could assist in long‐term conservation strategy because fundamental metacommunity structural processes are resistant to environmental change.     

Connectivity and propagule sources composition drive ditch plant metacommunity structure

The fragmentation of agricultural landscapes has a major impact on biodiversity. In addition to habitat loss, dispersal limitation increasingly appears as a significant driver of biodiversity decline. Landscape linear elements, like ditches, may reduce the negative impacts of fragmentation by enhancing connectivity for many organisms, in addition to providing refuge habitats. To characterize these effects, we investigated the respective roles of propagule source composition and connectivity at the landscape scale on hydrochorous and non-hydrochorous ditch bank plant metacommunities. Twenty-seven square sites (0.5 km² each) were selected in an agricultural lowland of northern France. At each site, plant communities were sampled on nine ditch banks (totaling 243 ditches). Variables characterizing propagule sources composition and connectivity were calculated for landscape mosaic and ditch network models. The landscape mosaic influenced only non-hydrochorous species, while the ditch network impacted both hydrochorous and non-hydrochorous species. Non-hydrochorous metacommunities were dependent on a large set of land-use elements, either within the landscape mosaic or adjacent to the ditch network, whereas hydrochorous plant metacommunities were only impacted by the presence of ditches adjacent to crops and roads. Ditch network connectivity also influenced both hydrochorous and non-hydrochorous ditch bank plant metacommunity structure, suggesting that beyond favoring hydrochory, ditches may also enhance plant dispersal by acting on other dispersal vectors. Increasing propagule sources heterogeneity and connectivity appeared to decrease within-metacommunity similarity within landscapes. Altogether, our results suggest that the ditch network's composition and configuration impacts plant metacommunity structure by affecting propagule dispersal possibilities, with contrasted consequences depending on species' dispersal vectors.     

A general linear framework for the comparison and evaluation of models of sensorimotor synchronization

Sensorimotor synchronization (SMS), the temporal coordination of a rhythmic movement with an external rhythm, has been studied most often in tasks that require tapping along with a metronome. Models of SMS use information about the timing of preceding stimuli and responses to predict when the next response will be made. This article compares the theoretical structure and empirical predictions of four two-parameter models proposed in the literature: Michon (Timing in temporal tracking, Van Gorcum, Assen, 1967), Hary and Moore (Br J Math Stat Psychol 40:109–124, 1987b), Mates (Biol Cybern 70:463–473, 1994a; Biol Cybern 70:475–484, 1994b), and Schulze et al. (Mus Percept 22:461–467, 2005). By embedding these models within a general linear framework, the mathematical equivalence of the Michon, Hary and Moore, and Schulze et al. models is demonstrated. The Mates model, which differs from the other three, is then tested empirically with new data from a tapping experiment in which the metronome alternated between two tempi. The Mates model predictions are found to be invalid for about one-third of the trials, suggesting that at least one of the model’s underlying assumptions is incorrect. The other models cannot be refuted as easily, but they do not predict some features of the data very accurately. Comparison of the models’ predictions in a training/test procedure did not yield any significant differences. The general linear framework introduced here may help in the formulation of new models that make better predictions.     

A framework for multidimensional modelling of activity and structure of multispecies biofilms

Concepts from previous biofilm models were integrated to create a framework for the implementation of multidimensional (2D and 3D) multispecies biofilm models. The framework is here described at three levels: (i) mathematical representation of the processes involved in biofilm formation, (ii) numerical implementation into a computer program (freely available from our website http://www.biofilms.bt.tudelft.nl/frameworkMaterial) and (iii) using the program for the creation of biofilm models with multiple bacterial and solute species. An improved version of the individual-based modelling (IbM) that allows structured biomass was used. In this approach biomass composition may be discriminated into any number of particulate species, including extracellular polymeric substances (EPS) for which specific functionality was included. Detachment is also included, described as occurring at the biofilm surface with variable local rates derived from functions of state variables. The application of this modelling framework to a multispecies system with structured biomass is illustrated in a case study where the competition between an organism capable of accumulating polyhydroxybutyrate (PHB, an internal storage compound) and an EPS-producing organism in a two-species biofilm is analysed. Results illustrate that biofilms enriched in PHB-producing organisms may be obtained by supplying substrate intermittently in feast/famine cycles.     

A general computational framework for modeling cellular structure and function.

The "Virtual Cell" provides a general system for testing cell biological mechanisms and creates a framework for encapsulating the burgeoning knowledge base comprising the distribution and dynamics of intracellular biochemical processes. It approaches the problem by associating biochemical and electrophysiological data describing individual reactions with experimental microscopic image data describing their subcellular localizations. Individual processes are collected within a physical and computational infrastructure that accommodates any molecular mechanism expressible as rate equations or membrane fluxes. An illustration of the method is provided by a dynamic simulation of IP3-mediated Ca2+ release from endoplasmic reticulum in a neuronal cell. The results can be directly compared to experimental observations and provide insight into the role of experimentally inaccessible components of the overall mechanism.     

A framework for protein structure classification and identification of novel protein structures

Background  

Protein structure classification plays a central role in understanding the function of a protein molecule with respect to all known proteins in a structure database. With the rapid increase in the number of new protein structures, the need for automated and accurate methods for protein classification is increasingly important.