Molecular ecological network analyses

BACKGROUND

Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data.

RESULTS

Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA).

CONCLUSIONS

The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.

Distance-Based Tests for Homogeneity of Multivariate Dispersions

The traditional likelihood-based test for differences in multivariate dispersions is known to be sensitive to nonnormality. It is also impossible to use when the number of variables exceeds the number of observations. Many biological and ecological data sets have many variables, are highly skewed, and are zero-inflated. The traditional test and even some more robust alternatives are also unreasonable in many contexts where measures of dispersion based on a non-Euclidean dissimilarity would be more appropriate. Distance-based tests of homogeneity of multivariate dispersions, which can be based on any dissimilarity measure of choice, are proposed here. They rely on the rotational invariance of either the multivariate centroid or the spatial median to obtain measures of spread using principal coordinate axes. The tests are straightforward multivariate extensions of Levene's test, with P-values obtained either using the traditional F-distribution or using permutation of either least-squares or LAD residuals. Examples illustrate the utility of the approach, including the analysis of stabilizing selection in sparrows, biodiversity of New Zealand fish assemblages, and the response of Indonesian reef corals to an El Niño. Monte Carlo simulations from the real data sets show that the distance-based tests are robust and powerful for relevant alternative hypotheses of real differences in spread.

Source partitioning using stable isotopes: coping with too much variation

Background

Stable isotope analysis is increasingly being utilised across broad areas of ecology and biology. Key to much of this work is the use of mixing models to estimate the proportion of sources contributing to a mixture such as in diet estimation.

Methodology

By accurately reflecting natural variation and uncertainty to generate robust probability estimates of source proportions, the application of Bayesian methods to stable isotope mixing models promises to enable researchers to address an array of new questions, and approach current questions with greater insight and honesty.

Conclusions

We outline a framework that builds on recently published Bayesian isotopic mixing models and present a new open source R package, SIAR. The formulation in R will allow for continued and rapid development of this core model into an all-encompassing single analysis suite for stable isotope research.

Variation partitioning of species data matrices: estimation and comparison of fractions

Establishing relationships between species distributions and environmental characteristics is a major goal in the search for forces driving species distributions. Canonical ordinations such as redundancy analysis and canonical correspondence analysis are invaluable tools for modeling communities through environmental predictors. They provide the means for conducting direct explanatory analysis in which the association among species can be studied according to their common and unique relationships with the environmental variables and other sets of predictors of interest, such as spatial variables. Variation partitioning can then be used to test and determine the likelihood of these sets of predictors in explaining patterns in community structure. Although variation partitioning in canonical analysis is routinely used in ecological analysis, no effort has been reported in the literature to consider appropriate estimators so that comparisons between fractions or, eventually, between different canonical models are meaningful. In this paper, we show that variation partitioning as currently applied in canonical analysis is biased. We present appropriate unbiased estimators. In addition, we outline a statistical test to compare fractions in canonical analysis. The question addressed by the test is whether two fractions of variation are significantly different from each other. Such assessment provides an important step toward attaining an understanding of the factors patterning community structure. The test is shown to have correct Type I. error rates and good power for both redundancy analysis and canonical correspondence analysis.

Energy homeostasis as an integrative tool for assessing limits of environmental stress tolerance in aquatic invertebrates

Energy balance is a fundamental requirement of stress adaptation and tolerance. We explore the links between metabolism, energy balance and stress tolerance using aquatic invertebrates as an example and demonstrate that using key parameters of energy balance (aerobic scope for growth, reproduction and activity; tissue energy status; metabolic rate depression; and compensatory onset of anaerobiosis) can assist in integrating the effects of multiple stressors and their interactions and in predicting the whole-organism and population-level consequences of environmental stress. We argue that limitations of both the amount of available energy and the rates of its acquisition and metabolic conversions result in trade-offs between basal maintenance of a stressed organism and energy costs of fitness-related functions such as reproduction, development and growth and can set limit to the tolerance of a broad range of environmental stressors. The degree of stress-induced disturbance of energy balance delineates transition from moderate stress compatible with population persistence (pejus range) to extreme stress where only time-limited existence is possible (pessimum range). It also determines the predominant adaptive strategy of metabolic responses (energy compensation vs. conservation) that allows an organism to survive the disturbance. We propose that energy-related biomarkers can be used to determine the conditions when these metabolic transitions occur and thus predict ecological consequences of stress exposures. Bioenergetic considerations can also provide common denominator for integrating stress responses and predicting tolerance limits under the environmentally realistic scenarios when multiple and often variable stressors act simultaneously on an organism. Determination of bioenergetic sustainability at the organism's level (or lack thereof) has practical implications. It can help identify the habitats and/or conditions where a population can survive (even if at the cost of reduced reproduction and growth) and those that are incapable of supporting viable populations. Such an approach will assist in explaining and predicting the species' distribution limits in the face of the environmental change and informing the conservation efforts and resource management practices.

Predicting species distributions for conservation decisions

Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of 'translators' between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.

Why do we still use stepwise modelling in ecology and behaviour?

1. The biases and shortcomings of stepwise multiple regression are well established within the statistical literature. However, an examination of papers published in 2004 by three leading ecological and behavioural journals suggested that the use of this technique remains widespread: of 65 papers in which a multiple regression approach was used, 57% of studies used a stepwise procedure. 2. The principal drawbacks of stepwise multiple regression include bias in parameter estimation, inconsistencies among model selection algorithms, an inherent (but often overlooked) problem of multiple hypothesis testing, and an inappropriate focus or reliance on a single best model. We discuss each of these issues with examples. 3. We use a worked example of data on yellowhammer distribution collected over 4 years to highlight the pitfalls of stepwise regression. We show that stepwise regression allows models containing significant predictors to be obtained from each year's data. In spite of the significance of the selected models, they vary substantially between years and suggest patterns that are at odds with those determined by analysing the full, 4-year data set. 4. An information theoretic (IT) analysis of the yellowhammer data set illustrates why the varying outcomes of stepwise analyses arise. In particular, the IT approach identifies large numbers of competing models that could describe the data equally well, showing that no one model should be relied upon for inference.

Applications of next generation sequencing in molecular ecology of non-model organisms

As most biologists are probably aware, technological advances in molecular biology during the last few years have opened up possibilities to rapidly generate large-scale sequencing data from non-model organisms at a reasonable cost. In an era when virtually any study organism can 'go genomic', it is worthwhile to review how this may impact molecular ecology. The first studies to put the next generation sequencing (NGS) to the test in ecologically well-characterized species without previous genome information were published in 2007 and the beginning of 2008. Since then several studies have followed in their footsteps, and a large number are undoubtedly under way. This review focuses on how NGS has been, and can be, applied to ecological, population genetic and conservation genetic studies of non-model species, in which there is no (or very limited) genomic resources. Our aim is to draw attention to the various possibilities that are opening up using the new technologies, but we also highlight some of the pitfalls and drawbacks with these methods. We will try to provide a snapshot of the current state of the art for this rapidly advancing and expanding field of research and give some likely directions for future developments.

Can stable isotope ratios provide for community-wide measures of trophic structure?

Stable isotope ratios (typically of carbon and nitrogen) provide one representation of an organism's trophic niche and are widely used to examine aspects of food web structure. Yet stable isotopes have not been applied to quantitatively characterize community-wide aspects of trophic structure (i.e., at the level of an entire food web). We propose quantitative metrics that can be used to this end, drawing on similar approaches from ecomorphology research. For example, the convex hull area occupied by species in delta13C-delta15N niche space is a representation of the total extent of trophic diversity within a food web, whereas mean nearest neighbor distance among all species pairs is a measure of species packing within trophic niche space. To facilitate discussion of opportunities and limitations of the metrics, we provide empirical and conceptual examples drawn from Bahamian tidal creek food webs. These examples illustrate how this methodology can be used to quantify trophic diversity and trophic redundancy in food webs, as well as to link individual species to characteristics of the food web in which they are embedded. Building from extensive applications of stable isotope ratios by ecologists, the community-wide metrics may provide a new perspective on food web structure, function, and dynamics.

Constructing, conducting and interpreting animal social network analysis

1. Animal social networks are descriptions of social structure which, aside from their intrinsic interest for understanding sociality, can have significant bearing across many fields of biology. 2. Network analysis provides a flexible toolbox for testing a broad range of hypotheses, and for describing the social system of species or populations in a quantitative and comparable manner. However, it requires careful consideration of underlying assumptions, in particular differentiating real from observed networks and controlling for inherent biases that are common in social data. 3. We provide a practical guide for using this framework to analyse animal social systems and test hypotheses. First, we discuss key considerations when defining nodes and edges, and when designing methods for collecting data. We discuss different approaches for inferring social networks from these data and displaying them. We then provide an overview of methods for quantifying properties of nodes and networks, as well as for testing hypotheses concerning network structure and network processes. Finally, we provide information about assessing the power and accuracy of an observed network. 4. Alongside this manuscript, we provide appendices containing background information on common programming routines and worked examples of how to perform network analysis using the r programming language. 5. We conclude by discussing some of the major current challenges in social network analysis and interesting future directions. In particular, we highlight the under-exploited potential of experimental manipulations on social networks to address research questions.

Bacterial toxicity comparison between nano- and micro-scaled oxide particles

Toxicity of nano-scaled aluminum, silicon, titanium and zinc oxides to bacteria (Bacillus subtilis, Escherichia coli and Pseudomonas fluorescens) was examined and compared to that of their respective bulk (micro-scaled) counterparts. All nanoparticles but titanium oxide showed higher toxicity (at 20 mg/L) than their bulk counterparts. Toxicity of released metal ions was differentiated from that of the oxide particles. ZnO was the most toxic among the three nanoparticles, causing 100% mortality to the three tested bacteria. Al(2)O(3) nanoparticles had a mortality rate of 57% to B. subtilis, 36% to E. coli, and 70% to P. fluorescens. SiO(2) nanoparticles killed 40% of B. subtilis, 58% of E. coli, and 70% of P. fluorescens. TEM images showed attachment of nanoparticles to the bacteria, suggesting that the toxicity was affected by bacterial attachment. Bacterial responses to nanoparticles were different from their bulk counterparts; hence nanoparticle toxicity mechanisms need to be studied thoroughly.

SPATIAL MODELING IN ECOLOGY: THE FLEXIBILITY OF EIGENFUNCTION SPATIAL ANALYSES

Recently, analytical approaches based on the eigenfunctions of spatial configuration matrices have been proposed in order to consider explicitly spatial predictors. The present study demonstrates the usefulness of eigenfunctions in spatial modeling applied to ecological problems and shows equivalencies of and differences between the two current implementations of this methodology. The two approaches in this category are the distance-based (DB) eigenvector maps proposed by P. Legendre and his colleagues, and spatial filtering based upon geographic connectivity matrices (i.e., topology-based; CB) developed by D. A. Griffith and his colleagues. In both cases, the goal is to create spatial predictors that can be easily incorporated into conventional regression models. One important advantage of these two approaches over any other spatial approach is that they provide a flexible tool that allows the full range of general and generalized linear modeling theory to be applied to ecological and geographical problems in the presence of nonzero spatial autocorrelation.

Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses

Microbes exist in a range of metabolic states (for example, dormant, active and growing) and analysis of ribosomal RNA (rRNA) is frequently employed to identify the 'active' fraction of microbes in environmental samples. While rRNA analyses are no longer commonly used to quantify a population's growth rate in mixed communities, due to rRNA concentration not scaling linearly with growth rate uniformly across taxa, rRNA analyses are still frequently used toward the more conservative goal of identifying populations that are currently active in a mixed community. Yet, evidence indicates that the general use of rRNA as a reliable indicator of metabolic state in microbial assemblages has serious limitations. This report highlights the complex and often contradictory relationships between rRNA, growth and activity. Potential mechanisms for confounding rRNA patterns are discussed, including differences in life histories, life strategies and non-growth activities. Ways in which rRNA data can be used for useful characterization of microbial assemblages are presented, along with questions to be addressed in future studies.

BOOSTED TREES FOR ECOLOGICAL MODELING AND PREDICTION

Accurate prediction and explanation are fundamental objectives of statistical analysis, yet they seldom coincide. Boosted trees are a statistical learning method that attains both of these objectives for regression and classification analyses. They can deal with many types of response variables (numeric, categorical, and censored), loss functions (Gaussian, binomial, Poisson, and robust), and predictors (numeric, categorical). Interactions between predictors can also be quantified and visualized. The theory underpinning boosted trees is presented, together with interpretive techniques. A new form of boosted trees, namely, "aggregated boosted trees" (ABT), is proposed and, in a simulation study, is shown to reduce prediction error relative to boosted trees. A regression data set is analyzed using ABT to illustrate the technique and to compare it with other methods, including boosted trees, bagged trees, random forests, and generalized additive models. A software package for ABT analysis using the R software environment is included in the Appendices together with worked examples.

Parasites dominate food web links

Parasitism is the most common animal lifestyle, yet food webs rarely include parasites. The few earlier studies have indicated that including parasites leads to obvious increases in species richness, number of links, and food chain length. A less obvious result was that adding parasites slightly reduced connectance, a key metric considered to affect food web stability. However, reported reductions in connectance after the addition of parasites resulted from an inappropriate calculation. Two alternative corrective approaches applied to four published studies yield an opposite result: parasites increase connectance, sometimes dramatically. In addition, we find that parasites can greatly affect other food web statistics, such as nestedness (asymmetry of interactions), chain length, and linkage density. Furthermore, whereas most food webs find that top trophic levels are least vulnerable to natural enemies, the inclusion of parasites revealed that mid-trophic levels, not low trophic levels, suffered the highest vulnerability to natural enemies. These results show that food webs are very incomplete without parasites. Most notably, recognition of parasite links may have important consequences for ecosystem stability because they can increase connectance and nestedness.

COEXISTENCE OF THE NICHE AND NEUTRAL PERSPECTIVES IN COMMUNITY ECOLOGY

The neutral theory for community structure and biodiversity is dependent on the assumption that species are equivalent to each other in all important ecological respects. We explore what this concept of equivalence means in ecological communities, how such species may arise evolutionarily, and how the possibility of ecological equivalents relates to previous ideas about niche differentiation. We also show that the co-occurrence of ecologically similar or equivalent species is not incompatible with niche theory as has been supposed, because niche relations can sometimes favor coexistence of similar species. We argue that both evolutionary and ecological processes operate to promote the introduction and to sustain the persistence of ecologically similar and in many cases nearly equivalent species embedded in highly structured food webs. Future work should focus on synthesizing niche and neutral perspectives rather than dichotomously debating whether neutral or niche models provide better explanations for community structure and biodiversity.

Lead isotopes in environmental sciences: a review

Lead (Pb) isotopic analyses proved to be a very efficient tool for tracing the sources of local and global Pb pollution. This review presents an overview of literature published on the use of Pb isotopic analyses of different environmental matrices (atmospheric aerosols, lichens, tree rings, peat deposits, lake, stream, marine sediments, soils, etc.). In order to gain more insight, the isotopic compositions of major sources of Pb in the environment as determined by several authors are described in detail. These include, above all, the former use of leaded gasoline, coal combustion, industrial activities (e.g., metallurgy) and waste incineration. Furthermore, this review summarises analytical techniques (especially ICP-MS) used for the determination of Pb isotopes in environmental samples.

Use of 16S rRNA and rpoB genes as molecular markers for microbial ecology studies

Several characteristics of the 16S rRNA gene, such as its essential function, ubiquity, and evolutionary properties, have allowed it to become the most commonly used molecular marker in microbial ecology. However, one fact that has been overlooked is that multiple copies of this gene are often present in a given bacterium. These intragenomic copies can differ in sequence, leading to identification of multiple ribotypes for a single organism. To evaluate the impact of such intragenomic heterogeneity on the performance of the 16S rRNA gene as a molecular marker, we compared its phylogenetic and evolutionary characteristics to those of the single-copy gene rpoB. Full-length gene sequences and gene fragments commonly used for denaturing gradient gel electrophoresis were compared at various taxonomic levels. Heterogeneity found between intragenomic 16S rRNA gene copies was concentrated in specific regions of rRNA secondary structure. Such "heterogeneity hot spots" occurred within all gene fragments commonly used in molecular microbial ecology. This intragenomic heterogeneity influenced 16S rRNA gene tree topology, phylogenetic resolution, and operational taxonomic unit estimates at the species level or below. rpoB provided comparable phylogenetic resolution to that of the 16S rRNA gene at all taxonomic levels, except between closely related organisms (species and subspecies levels), for which it provided better resolution. This is particularly relevant in the context of a growing number of studies focusing on subspecies diversity, in which single-copy protein-encoding genes such as rpoB could complement the information provided by the 16S rRNA gene.

A diagnostic approach for going beyond panaceas

The articles in this special feature challenge the presumption that scholars can make simple, predictive models of social-ecological systems (SESs) and deduce universal solutions, panaceas, to problems of overuse or destruction of resources. Moving beyond panaceas to develop cumulative capacities to diagnose the problems and potentialities of linked SESs requires serious study of complex, multivariable, nonlinear, cross-scale, and changing systems. Many variables have been identified by researchers as affecting the patterns of interactions and outcomes observed in empirical studies of SESs. A step toward developing a diagnostic method is taken by organizing these variables in a nested, multitier framework. The framework enables scholars to organize analyses of how attributes of (i) a resource system (e.g., fishery, lake, grazing area), (ii) the resource units generated by that system (e.g., fish, water, fodder), (iii) the users of that system, and (iv) the governance system jointly affect and are indirectly affected by interactions and resulting outcomes achieved at a particular time and place. The framework also enables us to organize how these attributes may affect and be affected by larger socioeconomic, political, and ecological settings in which they are embedded, as well as smaller ones. The framework is intended to be a step toward building a strong interdisciplinary science of complex, multilevel systems that will enable future diagnosticians to match governance arrangements to specific problems embedded in a social-ecological context.

Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data

Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called ‘early warning signals’, and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data.

Use of fecal glucocorticoid metabolite measures in conservation biology research: considerations for application and interpretation

Fecal glucocorticoid metabolite analyses are increasingly being used by a variety of scientists (e.g., conservation biologists, animal scientists) to examine glucocorticoid (i.e., stress hormone) secretion in domestic and wild vertebrates. Adrenocortical activity (i.e., stress response) is of interest to conservation biologists because stress can alter animal behavior, reduce resistance to disease, and affect population performance. The noninvasiveness of fecal-based assessments is attractive, particularly when studying endangered species, because samples can often be obtained without disturbing the animal. Despite such advantages, many confounding factors inhibit the utility of this technique in addressing conservation problems. In particular, interpretation of fecal glucocorticoid metabolite (FGM) measures may be confounded by the length of time animals are held in captivity, normal seasonal and daily rhythms, body condition, sample storage and treatment techniques, diet of the animal, assay selection, animal status (i.e., social ranking, reproductive status), sample age and condition, and sample mass. Further complicating interpretation and utility of these measures is the apparent species-specific response to these factors. The purpose of this paper is to discuss the factors that confound interpretation of FGM measures, summarize research that addresses these issues, and offer an agenda for future research and interpretation. We urge conservation biologists to carefully consider confounding factors and the relationship between FGM secretion and population performance and biological costs when investigating effects of environmental and human-induced disturbances on wildlife. The crisis nature of many decisions in conservation biology often requires decisions from limited data; however, confirmatory results should not be posited when data are incomplete or confounding factors are not understood. Building reliable databases, and research with surrogate species when possible, will aid future efforts and enhance the utility of FGM assays.