Genomics-fueled approaches to current challenges in marine ecology

As a result of recent advances in genomic technology, a 'tool set' is emerging that can be used by marine ecologists to gain new perspectives on central questions in the field. Techniques such as gene expression profiling using DNA microarrays, when placed in an ecological context, stand to advance greatly our understanding of how organisms respond to abiotic and biotic stresses. Here, we target areas in which adding genomics to ecological and physiological investigations will significantly advance our understanding of crucial issues ranging from the general biological effects of environmental temperature changes on individuals and communities to the interactions between symbionts in coral bleaching. At a time when both short- and long-term perturbations of marine ecosystems are increasing in severity, this kind of integrative approach could generate new and exciting hypotheses about the functioning of marine organisms and communities.     

Reintroduction: challenges and lessons for basic ecology

Ecology is a subject where theoretical predictions are often difficult to test experimentally in the field. To address this challenge, the Ecological Society of America suggested exploiting large-scale environmental management decisions in a scientific way. This 'adaptive management' constitutes one of the purposes of the Sustainable Biosphere Initiative. Meanwhile, in the current context of the biodiversity crisis, translocations and particularly reintroductions of threatened species are becoming more numerous. It is time for ecologists and wildlife managers to collaborate on these unique opportunities for large-scale studies.     

Multimodel inference in ecology and evolution: challenges and solutions

Information theoretic approaches and model averaging are increasing in popularity, but this approach can be difficult to apply to the realistic, complex models that typify many ecological and evolutionary analyses. This is especially true for those researchers without a formal background in information theory. Here, we highlight a number of practical obstacles to model averaging complex models. Although not meant to be an exhaustive review, we identify several important issues with tentative solutions where they exist (e.g. dealing with collinearity amongst predictors; how to compute model-averaged parameters) and highlight areas for future research where solutions are not clear (e.g. when to use random intercepts or slopes; which information criteria to use when random factors are involved). We also provide a worked example of a mixed model analysis of inbreeding depression in a wild population. By providing an overview of these issues, we hope that this approach will become more accessible to those investigating any process where multiple variables impact an evolutionary or ecological response.     

Integrating microbial ecology into ecosystem models: challenges and priorities

Microbial communities can potentially mediate feedbacks between global change and ecosystem function, owing to their sensitivity to environmental change and their control over critical biogeochemical processes. Numerous ecosystem models have been developed to predict global change effects, but most do not consider microbial mechanisms in detail. In this idea paper, we examine the extent to which incorporation of microbial ecology into ecosystem models improves predictions of carbon (C) dynamics under warming, changes in precipitation regime, and anthropogenic nitrogen (N) enrichment. We focus on three cases in which this approach might be especially valuable: temporal dynamics in microbial responses to environmental change, variation in ecological function within microbial communities, and N effects on microbial activity. Four microbially-based models have addressed these scenarios. In each case, predictions of the microbial-based models differ—sometimes substantially—from comparable conventional models. However, validation and parameterization of model performance is challenging. We recommend that the development of microbial-based models must occur in conjunction with the development of theoretical frameworks that predict the temporal responses of microbial communities, the phylogenetic distribution of microbial functions, and the response of microbes to N enrichment.     

Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges

Global positioning system (GPS) telemetry technology allows us to monitor and to map the details of animal movement, securing vast quantities of such data even for highly cryptic organisms. We envision an exciting synergy between animal ecology and GPS-based radiotelemetry, as for other examples of new technologies stimulating rapid conceptual advances, where research opportunities have been paralleled by technical and analytical challenges. Animal positions provide the elemental unit of movement paths and show where individuals interact with the ecosystems around them. We discuss how knowing where animals go can help scientists in their search for a mechanistic understanding of key concepts of animal ecology, including resource use, home range and dispersal, and population dynamics. It is probable that in the not-so-distant future, intense sampling of movements coupled with detailed information on habitat features at a variety of scales will allow us to represent an animal''s cognitive map of its environment, and the intimate relationship between behaviour and fitness. An extended use of these data over long periods of time and over large spatial scales can provide robust inferences for complex, multi-factorial phenomena, such as meta-analyses of the effects of climate change on animal behaviour and distribution.     

The diversity,ecology and evolution of extrafloral nectaries: current perspectives and future challenges

Background

Plants in over one hundred families in habitats worldwide bear extrafloral nectaries (EFNs). EFNs display a remarkable diversity of evolutionary origins, as well as diverse morphology and location on the plant. They secrete extrafloral nectar, a carbohydrate-rich food that attracts ants and other arthropods, many of which protect the plant in return. By fostering ecologically important protective mutualisms, EFNs play a significant role in structuring both plant and animal communities. And yet researchers are only now beginning to appreciate their importance and the range of ecological, evolutionary and morphological diversity that EFNs exhibit.

Scope

This Highlight features a series of papers that illustrate some of the newest directions in the study of EFNs. Here, we introduce this set of papers by providing an overview of current understanding and new insights on EFN diversity, ecology and evolution. We highlight major gaps in our current knowledge, and outline future research directions.

Conclusions

Our understanding of the roles EFNs play in plant biology is being revolutionized with the use of new tools from developmental biology and genomics, new modes of analysis allowing hypothesis-testing in large-scale phylogenetic frameworks, and new levels of inquiry extending to community-scale interaction networks. But many central questions remain unanswered; indeed, many have not yet been asked. Thus, the EFN puzzle remains an intriguing challenge for the future.     

Microarray methods in Drosophila neurobiology

In the relatively short period since their development, DNA microarrays have been used increasingly in the study of genetic and cellular processes, thereby offering a genome-wide approach to gene expression studies. With the advent of genome sequencing programs for organisms from yeast to man, the number of organisms which now have ready-made commercial arrays continues to increase. Here, the principle of DNA microarrays is introduced, with particular attention being given to the role of this technology in studies of the nervous system of the fruitfly Drosophila melanogaster. The importance of experimental design and sample preparation, in line with minimum information about microarray experiment (MIAME) compliance, is emphasised. The technical platforms available to the Drosophila neurobiologist have been illustrated and a brief number of data analysis tools that are readily available reviewed.     

基因芯片制备方法研究进展

基因芯片是融微电子学、生命科学和物理学为一体的技术,目前广泛应用于疾病的基因诊断、基因表达研究、基因组研究、发现新基因以及病原体的诊断,具有广阔的应用前景。基因芯片的制备主要可分为原位合成、合成后交联二种方法。本文综述了基因芯片的制备方法分析了各自的优缺点。     

Microarray Data Analysis

The analysis of differential gene expression in microarray experiments requires the development of adequate statistical tools. This article describes a simple statistical method for detecting differential expression between two conditions with a low number of replicates. When comparing two group means using a traditional t-test, gene-specific variance estimates are unstable and can lead to wrong conclusions. We construct a likelihood ratio test while modelling these variances hierarchically across all genes, and express it as a t-test statistic. By borrowing information across genes we can take advantage of their large numbers, and still yield a gene-specific test statistic. We show that this hierarchical t-test is more powerful than its traditional version and generates less false positives in a simulation study, especially with small sample sizes. This approach can be extended to cases where there are more than two groups.     

Split-Plot Microarray Experiments

This article focuses on microarray experiments with two or more factors in which treatment combinations of the factors corresponding to the samples paired together onto arrays are not completely random. A main effect of one (or more) factor(s) is confounded with arrays (the experimental blocks). This is called a split-plot microarray experiment. We utilise an analysis of variance (ANOVA) model to assess differentially expressed genes for between-array and within-array comparisons that are generic under a split-plot microarray experiment. Instead of standard t- or F-test statistics that rely on mean square errors of the ANOVA model, we use a robust method, referred to as 'a pooled percentile estimator', to identify genes that are differentially expressed across different treatment conditions. We illustrate the design and analysis of split-plot microarray experiments based on a case application described by Jin et al. A brief discussion of power and sample size for split-plot microarray experiments is also presented.     

微阵列生物芯片技术

综述了微阵列生物芯片的制备方法、原理以及应用,并讨论了今后的发展趋势。     

Microarray data assembler

SUMMARY: Large volumes of microarray data are generated and deposited in public databases. Most of this data is in the form of tab-delimited text files or Excel spreadsheets. Combining data from several of these files to reanalyze these data sets is time consuming. Microarray Data Assembler is specifically designed to simplify this task. The program can list files and data sources, convert selected text files into Excel files and assemble data across multiple Excel worksheets and workbooks. This program thus makes data assembling easy, saves time and helps avoid manual error. AVAILABILITY: The program is freely available for non-profit use, via email request from the author, after signing a Material Transfer Agreement with Johns Hopkins University.     

Warming reduces metabolic rate in marine snails: adaptation to fluctuating high temperatures challenges the metabolic theory of ecology

The universal temperature-dependence model (UTD) of the metabolic theory of ecology (MTE) proposes that temperature controls mass-scaled, whole-animal resting metabolic rate according to the first principles of physics (Boltzmann kinetics). Controversy surrounds the model''s implication of a mechanistic basis for metabolism that excludes the effects of adaptive regulation, and it is unclear how this would apply to organisms that live in fringe environments and typically show considerable metabolic adaptation. We explored thermal scaling of metabolism in a rocky-shore eulittoral-fringe snail (Echinolittorina malaccana) that experiences constrained energy gain and fluctuating high temperatures (between 25°C and approximately 50°C) during prolonged emersion (weeks). In contrast to the prediction of the UTD model, metabolic rate was often negatively related to temperature over a benign range (30–40°C), the relationship depending on (i) the temperature range, (ii) the degree of metabolic depression (related to the quiescent period), and (iii) whether snails were isolated within their shells. Apparent activation energies (E) varied between 0.05 and −0.43 eV, deviating excessively from the UTD''s predicted range of between 0.6 and 0.7 eV. The lowering of metabolism when heated should improve energy conservation in a high-temperature environment and challenges both the theory''s generality and its mechanistic basis.     

A plea for more ecology in phytoplankton ecology

When looking for a pattern of phytoplankton behaviour across trophic gradients, we need to cross the boundaries between different disciplinary areas, from autoecology to systems ecology, because eutrophication is a complex process which involves different time scales and different levels of community structure. Thus, we submit our observations to the muddled conceptual world of assemblage ecology. These inaccuracies arise, for example, from both species and community arguments; eutrophication as a fertilization or a metabolic phenomenon; and the notions frequently interwoven of pattern, process and rules. We suggest that it is advantageous to tackle this issue from the perspective of general ecology, rather than from a specifically planktonic orientation. In this way, useful general ecological tools, for example, time series and assembly-rule studies, can be used. Time-series study allows the dynamics of any variable to be described or to show that long term variable fluctuations may sometimes be unregulated, in response to some exogenous factor. Rules of assembly help us to resolve which traits are selectively involved during the eutrophication process. In this context, we advocate (1) the use of traits instead of morphospecies in phytoplankton studies, (2) looking for the dynamic patterns of phytoplankton with eutrophication, (3) the use of time series techniques to study phytoplankton trajectories, (4) the use of assembly rules to discern patterns in the formation of multispecies assemblages, (5) the consideration of the pelagic food-web in studies of phytoplankton dynamics and, as an overall suggestion, to borrow knowledge and inspiration from general ecology.     

Microarray Applications in Microbial Ecology Research

Microarray technology has the unparalleled potential to simultaneously determine the dynamics and/or activities of most, if not all, of the microbial populations in complex environments such as soils and sediments. Researchers have developed several types of arrays that characterize the microbial populations in these samples based on their phylogenetic relatedness or functional genomic content. Several recent studies have used these microarrays to investigate ecological issues; however, most have only analyzed a limited number of samples with relatively few experiments utilizing the full high-throughput potential of microarray analysis. This is due in part to the unique analytical challenges that these samples present with regard to sensitivity, specificity, quantitation, and data analysis. This review discusses specific applications of microarrays to microbial ecology research along with some of the latest studies addressing the difficulties encountered during analysis of complex microbial communities within environmental samples. With continued development, microarray technology may ultimately achieve its potential for comprehensive, high-throughput characterization of microbial populations in near real time.

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