What Do Experts and Novices ��See�� in Evolutionary Problems?

Considerable research has focused on differences in expert and novice problem representation and performance within physics, chemistry, and genetics. Here, we examine whether models of problem solving based on this work are useful within the domain of evolutionary biology. We utilized card sort tasks, interviews, and paper-and-pencil tests to: (1) delineate problem categorization rules, (2) quantify problem solving success, and (3) measure the relationships between the composition, structure, and coherence of problem solutions. We found that experts and novices perceived different item features to be of significance in card sort tasks, and that sensitivity to item surface features was adversely associated with problem solving success. As in other science domains, evolutionary problem representation and problem solving performance were tightly coupled. Explanatory coherence and the absence of cognitive biases were distinguishing features of evolutionary expertise. We discuss the implications of these findings for biology teaching and learning.     

You Don’t See What I See: Individual Differences in the Perception of Meaning from Visual Stimuli

Everyone has their own unique version of the visual world and there has been growing interest in understanding the way that personality shapes one’s perception. Here, we investigated meaningful visual experiences in relation to the personality dimension of schizotypy. In a novel approach to this issue, a non-clinical sample of subjects (total n = 197) were presented with calibrated images of scenes, cartoons and faces of varying visibility embedded in noise; the spatial properties of the images were constructed to mimic the natural statistics of the environment. In two experiments, subjects were required to indicate what they saw in a large number of unique images, both with and without actual meaningful structure. The first experiment employed an open-ended response paradigm and used a variety of different images in noise; the second experiment only presented a series of faces embedded in noise, and required a forced-choice response from the subjects. The results in all conditions indicated that a high positive schizotypy score was associated with an increased tendency to perceive complex meaning in images comprised purely of random visual noise. Individuals high in positive schizotypy seemed to be employing a looser criterion (response bias) to determine what constituted a ‘meaningful’ image, while also being significantly less sensitive at the task than those low in positive schizotypy. Our results suggest that differences in perceptual performance for individuals high in positive schizotypy are not related to increased suggestibility or susceptibility to instruction, as had previously been suggested. Instead, the observed reductions in sensitivity along with increased response bias toward seeing something that is not there, indirectly implicated subtle neurophysiological differences associated with the personality dimension of schizotypy, that are theoretically pertinent to the continuum of schizophrenia and hallucination-proneness.     

Is There Selective Mating in Tetrahymena During Genomic Exclusion?1

SYNOPSIS. Genomic exclusion is characterized by 2 rounds of mating. If exconjugants from different pairs remated at random after the first mating, we would expect a 1:2:1 ratio for genes present in heterozygous condition in the normal parent. An excess of homozygotes is observed which is similar for 2 different genes, suggesting that 10% of the rematings occur between exconjugants from the same Round 1 pair. Some but not all of these homozygotes can be attributed to a lack of separation of mates after the first round of mating. The rest may result because of differential mortality, induced autogamy or preferential remating.     

What Is the Future for Zoos and Aquariums?

ABSTRACT

Animal welfare concerns have plagued the professional zoo and aquarium field for decades. Societal differences remain concerning the well-being of animals, but it appears a shift is emerging. Scientific studies of animal welfare have dramatically increased, establishing that many previous concerns were not misguided public empathy or anthropomorphism. As a result, both zoo and aquarium animal welfare policy and science are now at the center of attention within the world’s professional zoos and aquariums. It is now possible to view a future that embraces the well-being of individual captive exotic animals, as well as that of their species, and one in which professional zoos and aquariums are dedicated equally to advancing both. Though the ethics of keeping exotic animals and animals from the wild in captivity are still a contentious subject both outside and even within the profession, this study argues. We argue that this path forward will substantially improve most zoo and aquarium animals' welfare and could significantly reduce societal concerns. If animal welfare science and policy are strongly rooted in compassion and embedded in robust accreditation systems, the basic zoo/aquarium paradigm will move toward a more thoughtful approach to the interface between visitors and animals. It starts with a fundamental commitment to the welfare of individual animals.     

What Is a Story?

This paper attempts to discover the criteria by which a listener accepts or rejects an item of oral narrative as being a story. The hypotheses are: (1) such criteria relate to structure rather than content, although the listener does not consciously distinguish structure from content; (2) the structure must have a certain minimal and maximal degree of complexity and be of a certain kind; (3) the criteria will hold cross-culturally. Information gathered from ethnographies and folklore literature is inadequate to confirm the hypotheses, but it does not contradict them. An experiment to test hypotheses (1) and (2) produced a certain degree of negative confirmation: listeners rejected as a story any narrative item which did not conform to the structural criteria regarded as minimal by myself; however, they also rejected items which I regarded as well structured but which were bizarre or nonsensical in content. The confusion of structure and content tended to confirm hypothesis (1).     

What Is Phloem Unloading?

Several studies of phloem unloading have failed to distinguish between transport events occurring at the sieve element/companion cell boundary and subsequent short-distance transport through parenchyma cells. Indirect evidence has been obtained for symplastic unloading in storage and utilization sinks. In other sinks transfer to the apoplast may occur, but not necessarily at the sieve element/companion cell complex, and the evidence for apoplastic phloem unloading is equivocal, as is the role of apoplastic acid invertase in this process. The ability of several types of sink cells to accumulate sugars from the apoplast is discussed in the conflicting light of functional symplastic continuity between sink cells. Attention is drawn to the complexity of the postunloading pathway in many sinks and the difficulty of determining the exact sites of symplast/apoplast solute exchange. Potential future areas for study in the field are highlighted.     

What Can Article-Level Metrics Do for You?

Article-level metrics (ALMs) provide a wide range of metrics about the uptake of an individual journal article by the scientific community after publication. They include citations, usage statistics, discussions in online comments and social media, social bookmarking, and recommendations. In this essay, we describe why article-level metrics are an important extension of traditional citation-based journal metrics and provide a number of example from ALM data collected for PLOS Biology.The scientific impact of a particular piece of research is reflected in how this work is taken up by the scientific community. The first systematic approach that was used to assess impact, based on the technology available at the time, was to track citations and aggregate them by journal. This strategy is not only no longer necessary—since now we can easily track citations for individual articles—but also, and more importantly, journal-based metrics are now considered a poor performance measure for individual articles [1],[2]. One major problem with journal-based metrics is the variation in citations per article, which means that a small percentage of articles can skew, and are responsible for, the majority of the journal-based citation impact factor, as shown by Campbell [1] for the 2004 Nature Journal Impact Factor. Figure 1 further illustrates this point, showing the wide distribution of citation counts between PLOS Biology research articles published in 2010. PLOS Biology research articles published in 2010 have been cited a median 19 times to date in Scopus, but 10% of them have been cited 50 or more times, and two articles [3],[4] more than 300 times. PLOS Biology metrics are used as examples throughout this essay, and the dataset is available in the supporting information (Data S1). Similar data are available for an increasing number of other publications and organizations.Open in a separate windowFigure 1Citation counts for PLOS Biology articles published in 2010.Scopus citation counts plotted as a probability distribution for all 197 PLOS Biology research articles published in 2010. Data collected May 20, 2013. Median 19 citations; 10% of papers have at least 50 citations.Scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator [2],[5],[6]. To this end, PLOS has collected and displayed a variety of metrics for all its articles since 2009. The array of different categorised article-level metrics (ALMs) used and provided by PLOS as of August 2013 are shown in Figure 2. In addition to citations and usage statistics, i.e., how often an article has been viewed and downloaded, PLOS also collects metrics about: how often an article has been saved in online reference managers, such as Mendeley; how often an article has been discussed in its comments section online, and also in science blogs or in social media; and how often an article has been recommended by other scientists. These additional metrics provide valuable information that we would miss if we only consider citations. Two important shortcomings of citation-based metrics are that (1) they take years to accumulate and (2) citation analysis is not always the best indicator of impact in more practical fields, such as clinical medicine [7]. Usage statistics often better reflect the impact of work in more practical fields, and they also sometimes better highlight articles of general interest (for example, the 2006 PLOS Biology article on the citation advantage of Open Access articles [8], one of the 10 most-viewed articles published in PLOS Biology).Open in a separate windowFigure 2Article-level metrics used by PLOS in August 2013 and their categories.Taken from [10] with permission by the authors.A bubble chart showing all 2010 PLOS Biology articles (Figure 3) gives a good overview of the year''s views and citations, plus it shows the influence that the article type (as indicated by dot color) has on an article''s performance as measured by these metrics. The weekly PLOS Biology publication schedule is reflected in this figure, with articles published on the same day present in a vertical line. Figure 3 also shows that the two most highly cited 2010 PLOS Biology research articles are also among the most viewed (indicated by the red arrows), but overall there isn''t a strong correlation between citations and views. The most-viewed article published in 2010 in PLOS Biology is an essay on Darwinian selection in robots [9]. Detailed usage statistics also allow speculatulation about the different ways that readers access and make use of published literature; some articles are browsed or read online due to general interest while others that are downloaded (and perhaps also printed) may reflect the reader''s intention to look at the data and results in detail and to return to the article more than once.Open in a separate windowFigure 3Views vs. citations for PLOS Biology articles published in 2010.All 304 PLOS Biology articles published in 2010. Bubble size correlates with number of Scopus citations. Research articles are labeled green; all other articles are grey. Red arrows indicate the two most highly cited papers. Data collected May 20, 2013.When readers first see an interesting article, their response is often to view or download it. By contrast, a citation may be one of the last outcomes of their interest, occuring only about 1 in 300 times a PLOS paper is viewed online. A lot of things happen in between these potential responses, ranging from discussions in comments, social media, and blogs, to bookmarking, to linking from websites. These activities are usually subsumed under the term “altmetrics,” and their variety can be overwhelming. Therefore, it helps to group them together into categories, and several organizations, including PLOS, are using the category labels of Viewed, Cited, Saved, Discussed, and Recommended (Figures 2 and ​and4,4, see also [10]).Open in a separate windowFigure 4Article-level metrics for PLOS Biology.Proportion of all 1,706 PLOS Biology research articles published up to May 20, 2013 mentioned by particular article-level metrics source. Colors indicate categories (Viewed, Cited, Saved, Discussed, Recommended), as used on the PLOS website.All PLOS Biology articles are viewed and downloaded, and almost all of them (all research articles and nearly all front matter) will be cited sooner or later. Almost all of them will also be bookmarked in online reference managers, such as Mendeley, but the percentage of articles that are discussed online is much smaller. Some of these percentages are time dependent; the use of social media discussion platforms, such as Twitter and Facebook for example, has increased in recent years (93% of PLOS Biology research articles published since June 2012 have been discussed on Twitter, and 63% mentioned on Facebook). These are the locations where most of the online discussion around published articles currently seems to take place; the percentage of papers with comments on the PLOS website or that have science blog posts written about them is much smaller. Not all of this online discussion is about research articles, and perhaps, not surprisingly, the most-tweeted PLOS article overall (with more than 1,100 tweets) is a PLOS Biology perspective on the use of social media for scientists [11].Some metrics are not so much indicators of a broad online discussion, but rather focus on highlighting articles of particular interest. For example, science blogs allow a more detailed discussion of an article as compared to comments or tweets, and journals themselves sometimes choose to highlight a paper on their own blogs, allowing for a more digestible explanation of the science for the non-expert reader [12]. Coverage by other bloggers also serves the same purpose; a good example of this is one recent post on the OpenHelix Blog [13] that contains video footage of the second author of a 2010 PLOS Biology article [14] discussing the turkey genome.F1000Prime, a commercial service of recommendations by expert scientists, was added to the PLOS Article-Level Metrics in August 2013. We now highlight on the PLOS website when any articles have received at least one recommendation within F1000Prime. We also monitor when an article has been cited within the widely used modern-day online encyclopedia, Wikipedia. A good example of the latter is the Tasmanian devil Wikipedia page [15] that links to a PLOS Biology research article published in 2010 [16]. While a F1000Prime recommendation is a strong endorsement from peer(s) in the scientific community, being included in a Wikipedia page is akin to making it into a textbook about the subject area and being read by a much wider audience that goes beyond the scientific community. PLOS Biology is the PLOS journal with the highest percentage of articles recommended in F1000Prime and mentioned in Wikipedia, but there is only partial overlap between the two groups of articles because they focus on different audiences (Figure 5). These recommendations and mentions in turn show correlations with other metrics, but not simple ones; you can''t assume, for example, that highly cited articles are more likely to be recommended by F1000Prime, so it will be interesting to monitor these trends now that we include this information.Open in a separate windowFigure 5 PLOS Biology articles: sites of recommendation and discussion.Number of PLOS Biology research articles published up to May 20, 2013 that have been recommended by F1000Prime (red) or mentioned in Wikipedia (blue).With the increasing availability of ALM data, there comes a growing need to provide tools that will allow the community to interrogate them. A good first step for researchers, research administrators, and others interested in looking at the metrics of a larger set of PLOS articles is the recently launched ALM Reports tool [17]. There are also a growing number of service providers, including Altmetric.com [18], ImpactStory [19], and Plum Analytics [20] that provide similar services for articles from other publishers.As article-level metrics become increasingly used by publishers, funders, universities, and researchers, one of the major challenges to overcome is ensuring that standards and best practices are widely adopted and understood. The National Information Standards Organization (NISO) was recently awarded a grant by the Alfred P. Sloan Foundation to work on this [21], and PLOS is actively involved in this project. We look forward to further developing our article-level metrics and to having them adopted by other publishers, which hopefully will pave the way to their wide incorporation into research and researcher assessments.     

What Use Is Population Genetics?

The Genetic Society of America’s Thomas Hunt Morgan Medal is awarded to an individual GSA member for lifetime achievement in the field of genetics. For over 40 years, 2015 recipient Brian Charlesworth has been a leader in both theoretical and empirical evolutionary genetics, making substantial contributions to our understanding of how evolution acts on genetic variation. Some of the areas in which Charlesworth’s research has been most influential are the evolution of sex chromosomes, transposable elements, deleterious mutations, sexual reproduction, and life history. He also developed the influential theory of background selection, whereby the recurrent elimination of deleterious mutations reduces variation at linked sites, providing a general explanation for the correlation between recombination rate and genetic variation.Open in a separate windowI am grateful to the Genetics Society of America for honoring me with the Thomas Hunt Morgan Medal and for inviting me to contribute this essay. I have spent nearly 50 years doing research in population genetics. This branch of genetics uses knowledge of the rules of inheritance to predict how the genetic composition of a population will change under the forces of evolution and compares the predictions to relevant data. As our knowledge of how genomes are organized and function has increased, so has the range of problems confronted by population geneticists. We are, however, a relatively small part of the genetics community, and sometimes it seems that our field is regarded as less important than those branches of genetics concerned with the properties of cells and individual organisms.I will take this opportunity to explain why I believe that population genetics is useful to a broad range of biologists. The fundamental importance of population genetics is the basic insights it provides into the mechanisms of evolution, some of which are far from intuitively obvious. Many of these insights came from the work of the first generation of population geneticists, notably Fisher, Haldane, and Wright. Their mathematical models showed that, contrary to what was believed by the majority of biologists in the 1920s, natural selection operating on Mendelian variation can cause evolutionary change at rates sufficient to explain historical patterns of evolution. This led to the modern synthesis of evolution (Provine 1971). No one can claim to understand how evolution works without some basic understanding of classical population genetics; those who do run the risk of making mistakes such as asserting that rapid evolutionary change is most likely to occur in small founder populations (Mayr 1954).

As our knowledge of how genomes are organized and function has increased, so has the range of problems confronted by population geneticists. We are, however, a relatively small part of the genetics community, and sometimes it seems that our field is regarded as less important than those branches of genetics concerned with the properties of cells and individual organisms.—B.C.

The modern synthesis is getting on for 80 years old, so this argument will probably not convince skeptical molecular geneticists that population genetics has a lot to offer the modern biologist. I provide two examples of the useful role that population genetic studies can play. First, one of the most notable discoveries of the past 40 years was the finding that the genomes of most species contain families of transposable elements (TEs) with the capacity to make new copies that insert elsewhere in the genome (Shapiro 1983). This led to two schools of thought about why they are present in the genome. One claimed that TEs are maintained because they confer benefits on the host by producing adaptively useful mutations (Syvanen 1984); the other believed that they are parasites, maintained by their ability to replicate within the genome despite potentially deleterious fitness effects of TE insertions (Doolittle and Sapienza 1980; Orgel and Crick 1980).The second hypothesis can be tested by comparing population genetic predictions with the results of TE surveys within populations. In the early 1980s, Chuck Langley, myself and several collaborators tried to do just this, using populations of Drosophila melanogaster (Charlesworth and Langley 1989). The models predicted that most Drosophila TEs should be found at low population frequencies at their insertion sites. This is so because D. melanogaster populations have large effective sizes (N_e). N_e is essentially the number of individuals that genetically contribute to the next generation. Large N_e means that a very small selection pressure can keep deleterious elements at low frequencies. This is a consequence of one of the most important findings of classical population genetics—the fate of a variant in a population is the product of N_e and the strength of selection (Fisher 1930; Kimura 1962). If, for example, N_e is 1000, a mutation that reduces fitness relative to wild type by 0.001 will be eliminated from the population with near certainty.Using the crude tools then available (restriction mapping of cloned genomic regions and in situ hybridization of labeled TE probes to polytene chromosomes), we found that nearly all TEs are indeed present at low frequencies in the population (Charlesworth and Langley 1989). Most of the exceptions to this rule were found in genomic regions in which little crossing over occurs (Maside et al. 2005). This is consistent with Chuck’s proposal that a major contributor to the removal of TEs from the population is selection against aneuploid progeny created by crossing over among homologous TEs at different locations in the genome (Langley et al. 1988). It is now a familiar finding that nonrecombining genomes or genomic regions tend to be full of TEs and other kinds of repetitive sequences; the population genetic reasons for this, discussed by Charlesworth et al. (1994), are perhaps not so familiar.Modern genomic methods provide much more powerful means for identifying TE insertions. Recent population surveys using these methods have confirmed the older findings: most TEs in Drosophila are present at low frequencies, and there is statistical evidence for selection against insertions (Barron et al. 2014). This is consistent with the existence of elaborate molecular mechanisms for repressing TE activity, such as the Piwi-interacting RNA (piRNA) pathway of animals (Senti and Brennecke 2010); there would be no reason to evolve such mechanisms if TEs were harmless. In a few cases, TEs have swept to high frequencies or fixation, and there is convincing evidence that at least some of these events are associated with increased fitness caused by the TE insertions themselves (Barron et al. 2014). These cases do not contradict the intragenomic parasite hypothesis for the maintenance of TEs; favorable mutations induced by TEs are too rare to outweigh the elimination of deleterious insertions unless new insertions continually replace those that are lost.

From the theory of aging, to the degeneration of Y chromosomes, to the dynamics of transposable elements, our understanding of the genetic basis of evolution is deeper and richer as a result of Charlesworth’s many contributions to the field. —Charles Langley, University of California, Davis

My other example is a population genetics discovery about a fundamental biological process: the PRDM9 protein involved in establishing recombination hot spots in humans. This was enabled by the revolution in population genetics brought about by coalescence theory (Hudson 1990), which is a powerful tool for looking at the statistical properties of a sample from a population under the hypothesis of selective neutrality. The basic idea is simple: if we sample two homologous, nonrecombining haploid genomes (e.g., mitochondrial DNA) from a large population, there is a probability of 1/(2N_e) that they are derived from the same parental genome in the preceding generation; i.e., they coalesce (N_e is the effective population size for the genome region in question). If they fail to coalesce in that generation, there is a probability of 1/(2N_e) that they coalesce one generation further back, and so on. If n genomes are sampled, there is a bifurcating tree connecting them back to their common ancestor. The size and shape of this tree are highly random, so genetically independent components of the genome experience different trees, even if they share the same N_e. The properties of sequence variability in the sample can be modeled by throwing mutations at random onto the tree (Hudson 1990).Recombination causes different sites in the genome to experience different trees, but closely linked sites have much more similar trees than independent sites. At the level of sequence variability, close linkage results in nonrandom associations between neutral variants—linkage disequilibrium (LD). The extent of LD among neutral variants at different sites is determined by the product of N_e and the frequency of recombination between them c (Ohta and Kimura 1971; McVean 2002). Richard Hudson proposed a statistical method for estimating N_ec from data on variants at multiple sites across the genome (Hudson 2001) that was implemented in a widely used computer program LDhat by Gil McVean and colleagues (McVean et al. 2002). Applications to large data sets on human sequence variability showed that the genome is full of recombination hot spots and cold spots, consistent with previous molecular genetic studies of specific loci (Myers et al. 2005). Most recombination occurs in hot spots and very little in between them, accounting for the fact that there is almost complete LD over tens or even hundreds of kilobases in humans. The identification of a large number of hot spots led to the discovery of a sequence motif bound by a zinc finger protein, PRDM9, at about the same time that mouse geneticists also discovered that PRDM9 promotes recombination (McVean and Myers 2010; Baudat et al. 2014). These discoveries have led to many interesting observations, such as associations between PRDM9 variants in humans and individual variation in recombination rates, generating an ongoing research program of great scientific interest (Baudat et al. 2014).With the ever-increasing use of genomic data, I am confident that many more such fruitful interactions between molecular and population genetics will take place. A take-home message is that more needs to be done to integrate training in population, molecular, and computational approaches to provide the next generation of researchers with the broad range of knowledge they will need.