Alternative statistical approaches to the use of data as evidence for hypotheses in human behavioral ecology

In their ambitious Evolutionary Anthropology paper, Winterhalder and Smith 1 review the history, theory, and methods of human behavioral ecology (HBE). In establishing how HBE differs from traditional approaches within sociocultural anthropology, they and others laud its hypothetical‐deductive research method. 1 - 3 Our aim is to critically examine how human behavioral ecologists conduct their research, specifically how they analyze and interpret data as evidence for scientific hypotheses. Through computer simulations and a review of empirical studies of human sex ratios, we consider some limitations of the status quo and present alternatives that could strengthen the field. In particular, we suggest that because human behavioral ecologists often consider multiple hypotheses, they should use statistical approaches that can quantify the evidence in empirical data for competing hypotheses. Although we focus on HBE, the principles of this paper apply broadly within biological anthropology.     

The Virtual Cell: a software environment for computational cell biology

The newly emerging field of computational cell biology requires software tools that address the needs of a broad community of scientists. Cell biological processes are controlled by an interacting set of biochemical and electrophysiological events that are distributed within complex cellular structures. Computational modeling is familiar to researchers in fields such as molecular structure, neurobiology and metabolic pathway engineering, and is rapidly emerging in the area of gene expression. Although some of these established modeling approaches can be adapted to address problems of interest to cell biologists, relatively few software development efforts have been directed at the field as a whole. The Virtual Cell is a computational environment designed for cell biologists as well as for mathematical biologists and bioengineers. It serves to aid the construction of cell biological models and the generation of simulations from them. The system enables the formulation of both compartmental and spatial models, the latter with either idealized or experimentally derived geometries of one, two or three dimensions.     

Design and analysis of bridging studies with prior probabilities on the null and alternative hypotheses

The pharmaceutical industry and regulatory agencies are increasingly interested in conducting bridging studies in order to bring an approved drug product from the original region (eg, United States or European Union) to a new region (eg, Asian-Pacific countries). In this article, we provide a new methodology for the design and analysis of bridging studies by assuming prior knowledge on how the null and alternative hypotheses in the original, foreign study are related to the null and alternative hypotheses in the bridging study and setting the type I error for the bridging study according to the strength of the foreign-study evidence. The new methodology accounts for randomness in the foreign-study evidence and controls the average type I error of the bridging study over all possibilities of the foreign-study evidence. In addition, the new methodology increases statistical power, when compared to approaches that do not use foreign-study evidence, and it allows for the possibility of not conducting the bridging study when the foreign-study evidence is unfavorable. Finally, we conducted extensive simulation studies to demonstrate the usefulness of the proposed methodology.     

Accidental experiments: ecological and evolutionary insights and opportunities derived from global change

Humans are the dominant ecological and evolutionary force on the planet today, transforming habitats, polluting environments, changing climates, introducing new species, and causing other species to decline in number or go extinct. These worrying anthropogenic impacts, collectively termed global change, are often viewed as a confounding factor to minimize in basic studies and a problem to resolve or quantify in applied studies. However, these ‘accidental experiments’ also represent opportunities to gain fundamental insight into ecological and evolutionary processes, especially when they result in perturbations that are large or long in duration and difficult or unethical to impose experimentally. We demonstrate this by describing important fundamental insights already gained from studies which utilize global change factors as accidental experiments. In doing so, we highlight why accidental experiments are sometimes more likely to yield insights than traditional approaches. Next, we argue that emerging environmental problems can provide even more opportunities for scientific discovery in the future, and provide both examples and guidelines for moving forward. We recommend 1) a greater flow of information between basic and applied subfields of ecology and evolution to identify emerging opportunities; 2) considering the advantages of the ‘accidental experiment’ approach relative to more traditional approaches; and 3) planning for the challenges inherent to uncontrolled accidental experiments. We emphasize that we do not view the accidental experiments provided by global change as replacements for scientific studies quantifying the magnitude of anthropogenic impacts or outlining strategies for mitigating impacts. Instead, we believe that accidental experiments are uniquely situated to provide insights into evolutionary and ecological processes that ultimately allow us to better predict and manage change on our human‐dominated planet. Synthesis Humans have an increasingly large impact on the planet. In response, ecologists and evolutionary biologists are dedicating increasing scientific attention to global change, largely with studies documenting biological effects and testing strategies to avoid or reverse negative impacts. In this article, we analyze global change from a different perspective, and suggest that human impacts on the environment also serve as valuable ‘accidental experiments’ that can provide fundamental scientific insight. We highlight and synthesize examples of studies taking this approach, and give guidance for gaining future insights from these unfortunate ‘accidental experiments’.     

Inference in ecology and evolution

Most ecologists and evolutionary biologists continue to rely heavily on null hypothesis significance testing, rather than on recently advocated alternatives, for inference. Here, we briefly review null hypothesis significance testing and its major alternatives. We identify major objectives of statistical analysis and suggest which analytical approaches are appropriate for each. Any well designed study can improve our understanding of biological systems, regardless of the inferential approach used. Nevertheless, an awareness of available techniques and their pitfalls could guide better approaches to data collection and broaden the range of questions that can be addressed. Although we should reduce our reliance on significance testing, it retains an important role in statistical education and is likely to remain fundamental to the falsification of scientific hypotheses.     

Rejecting "the given" in systematics

How morphology and systematics come together through morphological analysis, homology hypotheses and phylogenetic analysis is a topic of continuing debate. Some contemporary approaches reject biological evaluation of morphological characters and fall back on an atheoretical and putatively objective (but, in fact, phenetic) approach that defers to the test of congruence for homology assessment. We note persistent trends toward an uncritical empiricism (where evidence is believed to be immediately “given” in putatively theory‐free observation) and instrumentalism (where hypotheses of primary homology become mere instruments with little or no empirical foundation for choosing among competing phylogenetic hypotheses). We suggest that this situation is partly a consequence of the fact that the test of congruence and the related concept of total evidence have been inappropriately tied to a Popperian philosophy in modern systematics. Total evidence is a classical principle of inductive inference and does not imply a deductive test of homology. The test of congruence by itself is based philosophically on a coherence theory of truth (coherentism in epistemology), which is unconcerned with empirical foundation. We therefore argue that coherence of character statements (congruence of characters) is a necessary, but not a sufficient, condition to support or refute hypotheses of homology or phylogenetic relationship. There should be at least some causal grounding for homology hypotheses beyond mere congruence. Such causal grounding may be achieved, for example, through empirical investigations of comparative anatomy, developmental biology, functional morphology and secondary structure. © The Willi Hennig Society 2006.     

Popper,Laws, and the Exclusion of Biology from Genuine Science

The primary purpose of this paper is to argue that biologists should stop citing Karl Popper on what a genuinely scientific theory is. Various ways in which biologists cite Popper on this matter are surveyed, including the use of Popper to settle debates on methodology in phylogenetic systematics. It is then argued that the received view on Popper--namely, that a genuinely scientific theory is an empirically falsifiable one--is seriously mistaken, that Popper's real view was that genuinely scientific theories have the form of statements of laws of nature. It is then argued that biology arguably has no genuine laws of its own. In place of Popperian falsifiability, it is suggested that a cluster class epistemic values approach (which subsumes empirical falsifiability) is the best solution to the demarcation problem between genuine science and pseudo- or non-science.     

Community annotation in biology

   

Attempts to engage the scientific community to annotate biological data (such as protein/gene function) stored in databases have not been overly successful. There are several hypotheses on why this has not been successful but it is not clear which of these hypotheses are correct. In this study we have surveyed 50 biologists (who have recently published a paper characterizing a gene or protein) to better understand what would make them interested in providing input/contributions to biological databases. Based on our survey two things become clear: a) database managers need to proactively contact biologists to solicit contributions; and b) potential contributors need to be provided with an easy-to-use interface and clear instructions on what to annotate. Other factors such as 'reward' and 'employer/funding agency recognition' previously perceived as motivators was found to be less important. Based on this study we propose community annotation projects should devote resources to direct solicitation for input and streamlining of the processes or interfaces used to collect this input.     

Computational cell biologists snowed in at Cranwell.

Cell biology is being inundated by an avalanche of data from the genomics and proteomics enterprises. The complexity and sheer volume of information threaten to overwhelm the ability of traditional cell biologists to grasp its implications and develop experimentally testable hypotheses. For this reason, some have begun to explore computational approaches towards organizing complex data into quantitative models. This requires communication and collaboration between the biological science community and and the physical and mathematical sciences communities. A recent meeting [The First International Symposium on Computational Cell Biology, Cranwell Resort, Lenox, MA, USA; 4-6 March 2001. Organizers: J.H. Carson, A. Cowan, and L.M. Loew (www.nrcam.uchc.edu/conference).] made a first attempt to bring these two communities together. Three feet of new snow fell during the meeting, but the 125 attendees, an unusual mixture of cell biologists, computer scientists, mathematicians, physicists, and engineers, were having too much fun defining the new field of computational cell biology to notice that they were literally snowed in.     

Microarray data analysis: from disarray to consolidation and consensus

In just a few years, microarrays have gone from obscurity to being almost ubiquitous in biological research. At the same time, the statistical methodology for microarray analysis has progressed from simple visual assessments of results to a weekly deluge of papers that describe purportedly novel algorithms for analysing changes in gene expression. Although the many procedures that are available might be bewildering to biologists who wish to apply them, statistical geneticists are recognizing commonalities among the different methods. Many are special cases of more general models, and points of consensus are emerging about the general approaches that warrant use and elaboration.     

Cell biology, chemogenomics and chemoproteomics

The scientific techniques used in molecular biological research and drug discovery have changed dramatically over the past 10 years due to the influence of genomics, proteomics and bioinformatics. Furthermore, genomics and functional genomics are now merging into a new scientific approach called chemogenomics. Advancements in the study of molecular cell biology are dependent upon "omics" researchers realizing the importance of and using the experimental tools currently available to cell biologists. For example, novel microscopic techniques utilizing advanced computer imaging allow for the examination of live specimens in a fourth dimension, viz., time. Yet, molecular biologists have not taken full advantage of these and other traditional and novel cell biology techniques for the further advancement of genomic and proteomic-oriented research. The application of traditional and novel cellular biological techniques will enhance the science of genomics. The authors hypothesize that a stronger interdisciplinary approach must be taken between cell biology (and its closely related fields) and genomics, proteomics and bio-chemoinformatics. Since there is a lot of confusion regarding many of the "omics" definitions, this article also clarifies some of the basic terminology used in genomics, and related fields. It also reviews the current status and future potential of chemogenomics and its relationship to cell biology. The authors also discuss and expand upon the differences between chemogenomics and the relatively new term--chemoproteomics. We conclude that the advances in cell biology methods and approaches and their adoption by "omics" researchers will allow scientists to maximize our knowledge about life.     

Cultured meat: creative solutions for a cell biological problem

Cultured meat is an emerging technology that could address environmental, health, and animal welfare concerns associated with meat production. Development of cultured meat represents an exciting challenge for cell biologists and engineers, but it requires effective, open approaches for knowledge sharing to establish a fertile scientific field alongside a competitive industry.     

Life With or Without Names

The terms ‘life’, ‘species’ and ‘individuals’ are key concepts in biology. However, theoretical and practical concerns are directly associated with definitions of these terms and their use in researchers’ work. Although the practical implications of employing definition of ‘species’ and ‘individuals’ are often clear, it is surprising how most biologists work in their field of study without adhering to a specific definition of life. In everyday scientific practice, biologists rarely define life. This is somewhat understandable: the majority of biologists accept the standard definition of life without exploring it, but this represents a bad attitude. In this essay, we update the concepts of life, species, and individuals in the light of the new techniques for massive DNA sequencing collectively known as high throughput DNA sequencing (HTS). A re-evaluation of the newest approaches and traditional concepts is required, because in many scientific publications, HTS users apply concepts ambiguously (in particular that of species). However, the absence of clarity is understandable. For most of the last 250 years, from Linnaeus to the most recent researches, identification and classification have been performed applying the same process. On the contrary, through HTS, biologists have become simply identifiers, who construct boundaries around the biological entities and do not examine the taxa at length, resulting in uncertainty in most readers and displeasure in traditional taxonomists. We organised our essay to answer a basic question: can we develop new means to observe living organisms?     

THINK Back: KNowledge-based Interpretation of High Throughput data

Results of high throughput experiments can be challenging to interpret. Current approaches have relied on bulk processing the set of expression levels, in conjunction with easily obtained external evidence, such as co-occurrence. While such techniques can be used to reason probabilistically, they are not designed to shed light on what any individual gene, or a network of genes acting together, may be doing. Our belief is that today we have the information extraction ability and the computational power to perform more sophisticated analyses that consider the individual situation of each gene. The use of such techniques should lead to qualitatively superior results. The specific aim of this project is to develop computational techniques to generate a small number of biologically meaningful hypotheses based on observed results from high throughput microarray experiments, gene sequences, and next-generation sequences. Through the use of relevant known biomedical knowledge, as represented in published literature and public databases, we can generate meaningful hypotheses that will aide biologists to interpret their experimental data. We are currently developing novel approaches that exploit the rich information encapsulated in biological pathway graphs. Our methods perform a thorough and rigorous analysis of biological pathways, using complex factors such as the topology of the pathway graph and the frequency in which genes appear on different pathways, to provide more meaningful hypotheses to describe the biological phenomena captured by high throughput experiments, when compared to other existing methods that only consider partial information captured by biological pathways.     

Approximations, idealizations and 'experiments' at the physics-biology interface

This paper, which is based on recent empirical research at the University of Leeds, the University of Edinburgh, and the University of Bristol, presents two difficulties which arise when condensed matter physicists interact with molecular biologists: (1) the former use models which appear to be too coarse-grained, approximate and/or idealized to serve a useful scientific purpose to the latter; and (2) the latter have a rather narrower view of what counts as an experiment, particularly when it comes to computer simulations, than the former. It argues that these findings are related; that computer simulations are considered to be undeserving of experimental status, by molecular biologists, precisely because of the idealizations and approximations that they involve. The complexity of biological systems is a key factor. The paper concludes by critically examining whether the new research programme of 'systems biology' offers a genuine alternative to the modelling strategies used by physicists. It argues that it does not.     

Walking the tightrope: trends in African freshwater systematic ichthyology

Africa is blessed with an abundance and rich diversity of freshwater fishes, reflecting its Gondwanan history and geographical position astride the equator. Africa is, however, relatively poorly serviced scientifically, in this respect presenting a challenge to the tension between conserving biodiversity and sustainable development. Biosystematics has experienced several paradigm shifts in the past half century, including the rise of cladistics and more recently the adoption of molecular DNA applications to taxonomy and phylogeny and the assembly and manipulation of large data sets in an era of major development of bioinformatics. The richness of African biodiversity is a magnet to the global systematic community that, to a degree, offsets the disadvantage of an impoverished indigenous scientific capacity. Conservation biology, however, is rooted more closely to the local situation and therefore requires indigenous taxonomic services that are inevitably scarce. Balancing this network of tensions between scientific knowledge generation and application is like walking a tightrope for existing African scientific resources, and to cope it is essential to embrace modern innovative approaches such as barcoding to identify organisms. This paper considers the historical development of African freshwater ichthyology, presents a suite of recent examples illustrating trends in systematic ichthyology in Africa and draws conclusions to suggest that both traditional and new-age approaches to taxonomy are necessary for a complete understanding and appreciation of African freshwater fish diversity and its conservation. The chosen examples also suggest that the tensions between the approaches can be effectively managed provided exponents work collaboratively. The emerging evidence indicates that the combined skills and insight of complex scientific teams including systematists, ecologists, molecular biologists and earth scientists are needed to resolve the deep complexity of evolution in terms of space, time and form.     

Integrative taxonomy,or iterative taxonomy?

The recently introduced term ‘integrative taxonomy’ refers to taxonomy that integrates all available data sources to frame species limits. We survey current taxonomic methods available to delimit species that integrate a variety of data, including molecular and morphological characters. A literature review of empirical studies using the term ‘integrative taxonomy’ assessed the kinds of data being used to frame species limits, and methods of integration. Almost all studies are qualitative and comparative – we are a long way from a repeatable, quantitative method of truly ‘integrative taxonomy’. The usual methods for integrating data in phylogenetic and population genetic paradigms are not appropriate for integrative taxonomy, either because of the diverse range of data used or because of the special challenges that arise when working at the species/population boundary. We identify two challenges that, if met, will facilitate the development of a more complete toolkit and a more robust research programme in integrative taxonomy using species tree approaches. We propose the term ‘iterative taxonomy’ for current practice that treats species boundaries as hypotheses to be tested with new evidence. A search for biological or evolutionary explanations for discordant evidence can be used to distinguish between competing species boundary hypotheses. We identify two recent empirical examples that use the process of iterative taxonomy.     

Non-terrestrial origin of life: a transformative research paradigm shift

Theories and hypotheses in science are continually subject to verification, critical re-evaluation, revision and indeed evolution, in response to new observations and discoveries. Theories of the origin of life have been more constrained than other scientific theories and hypotheses in this regard, through the force of social and cultural pressures. There has been a tendency to adhere too rigidly to a class of theory that demands a purely terrestrial origin of life. For nearly five decades evidence in favour of a non-terrestrial origin of life and panspermia has accumulated which has not been properly assessed. A point has now been reached that demands the serious attention of biologists to a possibly transformative paradigm shift of the question of the origin of life, with profound implications across many disciplines.