Teleological reasoning,not acceptance of evolution,impacts students’ ability to learn natural selection

Background

How acceptance of evolution relates to understanding of evolution remains controversial despite decades of research. It even remains unclear whether cultural/attitudinal factors or cognitive factors have a greater impact on student ability to learn evolutionary biology. This study examined the influence of cultural/attitudinal factors (religiosity, acceptance of evolution, and parents’ attitudes towards evolution) and cognitive factors (teleological reasoning and prior understanding of natural selection) on students’ learning of natural selection over a semester-long undergraduate course in evolutionary medicine.

Method

Pre-post course surveys measured cognitive factors, including teleological reasoning and prior understanding of natural selection, and also cultural/attitudinal factors, including acceptance of evolution, parent attitudes towards evolution, and religiosity. We analyzed how these measures influenced increased understanding of natural selection over the semester.

Results

After controlling for other related variables, parent attitude towards evolution and religiosity predicted students’ acceptance of evolution, but did not predict students’ learning gains of natural selection over the semester. Conversely, lower levels of teleological reasoning predicted learning gains in understanding natural selection over the course, but did not predict students’ acceptance of evolution.

Conclusions

Acceptance of evolution did not predict students’ ability to learn natural selection over a semester in an evolutionary medicine course. However, teleological reasoning did impact students’ ability to learn natural selection.

     

Sexual networks: measuring sexual selection in structured,polyandrous populations

Sexual selection is traditionally measured at the population level, assuming that populations lack structure. However, increasing evidence undermines this approach, indicating that intrasexual competition in natural populations often displays complex patterns of spatial and temporal structure. This complexity is due in part to the degree and mechanisms of polyandry within a population, which can influence the intensity and scale of both pre- and post-copulatory sexual competition. Attempts to measure selection at the local and global scale have been made through multi-level selection approaches. However, definitions of local scale are often based on physical proximity, providing a rather coarse measure of local competition, particularly in polyandrous populations where the local scale of pre- and post-copulatory competition may differ drastically from each other. These limitations can be solved by social network analysis, which allows us to define a unique sexual environment for each member of a population: ‘local scale’ competition, therefore, becomes an emergent property of a sexual network. Here, we first propose a novel quantitative approach to measure pre- and post-copulatory sexual selection, which integrates multi-level selection with information on local scale competition derived as an emergent property of networks of sexual interactions. We then use simple simulations to illustrate the ways in which polyandry can impact estimates of sexual selection. We show that for intermediate levels of polyandry, the proposed network-based approach provides substantially more accurate measures of sexual selection than the more traditional population-level approach. We argue that the increasing availability of fine-grained behavioural datasets provides exciting new opportunities to develop network approaches to study sexual selection in complex societies.     

Individual behaviour and population dynamics: lessons from aphid parasitoids

An increasing number of researchers are studying behaviour in the hopes of understanding population dynamics or improving biological control efforts of insect pests by natural enemies. However, it is unclear exactly how behavioural studies will improve our understanding of these population level processes. In this paper we argue that in order to understand population level processes, the problem must be approached from a population biology perspective. A comprehensive understanding of certain behaviours will provide little towards our understanding of host-natural enemy interactions. For example, using an aphid-parasitoid model, we examined the effect that a commonly studied behaviour, variance in host selection by aphid parasitoids, has on aphid-parasitoid population dynamics. Differential host selection does not qualitatively alter classic Nicholson-Bailey dynamics, but only results in quantitative differences in aphid-parasitoid population sizes. Irrespective of the degree of aphid instar preference, a large increase in the number of aphids is followed by a large increase in the number of parasitoids, decimating the aphid population. Thus, studying some behaviours, such as variance in host selection, will not contribute substantially to an understanding of aphid-parasitoid population dynamics.     

Organizational levels analysis: a key to understanding processes in natural systems

Recent research conducted on a hypothetical four-parameter system generated by two stochastic processes has shown that errors of interpretation are likely to result when the relative importance of an underlying generating process is inferred from the analysis of an emergent pattern of a natural system. This paper presents an approach which substantially reduces that error for the hypothetical system, taken as an analog for a natural system. This approach is based on recognition of the organizational levels composing the system and utilizes observations on structure at a lower organizational level to resolve the properties of the generating processes at the lower level. The resulting knowledge of those underlying structures and the processes which created them are then used to determine the relative importance of those processes in affecting the emergent pattern of the system at a higher organizational level. It is concluded that in order to understand the role of a process in a natural system, measurements of system structure must be performed at a frequency which isolates the process from the other processes in the system.     

Programmed Learning in Biology

In a written test investigating the level of understanding of the concept of natural selection, only 18 per cent of a group of first-year university students with an Advanced-level biology background were consistently able to apply this concept to common environmental problems. In their explanations, over half the students mistakenly formulated a ‘theory of adaptation by induced mutation’ instead of a ‘theory of evolution by natural selection’. On further analysis, many students had a poor understanding of adaptation, immunity, the origin of mutations, and the laws of inheritance. A major cause of these interrelated errors was students extrapolating from changes occurring within the lifetime of an individual, to account for evolutionary changes altering populations over many generations.     

Students’ Mental Models of Evolutionary Causation: Natural Selection and Genetic Drift

In an effort to understand how to improve student learning about evolution, a focus of science education research has been to document and address students?? naive ideas. Less research has investigated how students reason about alternative scientific models that attempt to explain the same phenomenon (e.g., which causal model best accounts for evolutionary change?). Within evolutionary biology, research has yet to explore how non-adaptive factors are situated within students?? conceptual ecologies of evolutionary causation. Do students construct evolutionary explanations that include non-adaptive and adaptive factors? If so, how are non-adaptive factors structured within students?? evolutionary explanations? We used clinical interviews and two paper and pencil instruments (one open-response and one multiple-choice) to investigate the use of non-adaptive and adaptive factors in undergraduate students?? patterns of evolutionary reasoning. After instruction that included non-adaptive causal factors (e.g., genetic drift), we found them to be remarkably uncommon in students?? explanatory models of evolutionary change in both written assessments and clinical interviews. However, consistent with many evolutionary biologists?? explanations, when students used non-adaptive factors they were conceptualized as causal alternatives to selection. Interestingly, use of non-adaptive factors was not associated with greater understanding of natural selection in interviews or written assessments, or with fewer naive ideas of natural selection. Thus, reasoning using non-adaptive factors appears to be a distinct facet of evolutionary thinking. We propose a theoretical framework for an expert?Cnovice continuum of evolutionary reasoning that incorporates both adaptive and non-adaptive factors, and can be used to inform instructional efficacy in evolutionary biology.     

An active learning approach to investigate the ecosystem of tide flats using 3D modeling and printing

ABSTRACT

This paper presents an active learning approach that focuses on practical investigation of the ecosystem of tidal flats using 3D modeling and printing for biology students in order to enhance understanding of natural selection. The learning approach for the study followed a 5-step procedure: i) learning about 3D modeling and printing, ii) exploration of the ecosystem of tidal flats, iii) 3D designing of a bird beak, iv) 3D printing of a constructed beak, and v) a natural selection simulation activity. The learning method presented in this study centered on active student exploration of the tidal flats ecosystem using 3D modeling and printing. The learning approach presented in this paper could be implicated at schools to aid in students’ understanding of natural selection as it allows students to firsthand examine simulation changes to a bird beak and benthic communities. This study suggested the active learning method for natural selection as it incorporates student-designed exploration and direct investigative appraisal of the selection process.     

Social interaction in synthetic and natural microbial communities

Social interaction among cells is essential for multicellular complexity. But how do molecular networks within individual cells confer the ability to interact? And how do those same networks evolve from the evolutionary conflict between individual‐ and population‐level interests? Recent studies have dissected social interaction at the molecular level by analyzing both synthetic and natural microbial populations. These studies shed new light on the role of population structure for the evolution of cooperative interactions and revealed novel molecular mechanisms that stabilize cooperation among cells. New understanding of populations is changing our view of microbial processes, such as pathogenesis and antibiotic resistance, and suggests new ways to fight infection by exploiting social interaction. The study of social interaction is also challenging established paradigms in cancer evolution and immune system dynamics. Finding similar patterns in such diverse systems suggests that the same ‘social interaction motifs’ may be general to many cell populations.     

Addressing Undergraduate Student Misconceptions about Natural Selection with an Interactive Simulated Laboratory

Although evolutionary theory is considered to be a unifying foundation for biological education, misconceptions about basic evolutionary processes such as natural selection inhibit student understanding. Even after instruction, students harbor misconceptions about natural selection, suggesting that traditional teaching methods are insufficient for correcting these confusions. This has spurred an effort to develop new teaching methods and tools that effectively confront student misconceptions. In this study, we designed an interactive computer-based simulated laboratory to teach the principles of evolution through natural selection and to correct common student misconceptions about this process. We quantified undergraduate student misconceptions and understanding of natural selection before and after instruction with multiple-choice and open-response test questions and compared student performance across gender and academic levels. While our lab appeared to be effective at dispelling some common misconceptions about natural selection, we did not find evidence that it was as successful at increasing student mastery of the major principles of natural selection. Student performance varied across student academic level and question type, but students performed equally across gender. Beginner students were more likely to use misconceptions before instruction. Advanced students showed greater improvement than beginners on multiple-choice questions, while beginner students reduced their use of misconceptions in the open-response questions to a greater extent. These results suggest that misconceptions can be effectively addressed through computer-based simulated laboratories. Given the level of misconception use by beginner and advanced undergraduates and the gains in performance recorded after instruction at both academic levels, natural selection should continue to be reviewed through upper-level biology courses.     

The potential of a population genomics approach to analyse geographic mosaics of plant--insect coevolution

A central issue in the evolutionary ecology of species interactions is coevolution, which involves the reciprocal selection between individuals of interacting species. Understanding the importance of coevolution in shaping species interactions requires the consideration of spatial variation in their strength. This is exactly what the, recently developed, geographic mosaic theory of coevolution addresses. Another major development in the study of population ecology is the introduction of the population genomics approach in this field of research. This approach addresses spatial processes through molecular methods. It is of particular interest that population genomics is especially applicable to natural populations of non-model species. We describe how population genomics can be used in the context of the geographic mosaic of coevolution, specifically to identify coevolutionary hot-spots, and to attribute genetic variation found at specific loci to processes of selection versus trait remixing. The proposed integration of the population genomics approach with the conceptual framework of the geographic mosaic of coevolution is illustrated with a few selected, particularly demonstrative, examples from the realm of insect--plant interactions.     

Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo,Evo-Eco and Evolutionary Transitions

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions.     

No accident: genetic codes freeze in error-correcting patterns of the standard genetic code

The standard genetic code poses a challenge in understanding the evolution of information processing at a fundamental level of biological organization. Genetic codes are generally coadapted with, or 'frozen' by, the protein-coding genes that they translate, and so cannot easily change by natural selection. Yet the standard code has a significantly non-random pattern that corrects common errors in the transmission of information in protein-coding genes. Because of the freezing effect and for other reasons, this pattern has been proposed not to be due to selection but rather to be incidental to other evolutionary forces or even entirely accidental. We present results from a deterministic population genetic model of code-message coevolution. We explicitly represent the freezing effect of genes on genetic codes and the perturbative effect of changes in genetic codes on genes. We incorporate characteristic patterns of mutation and translational error, namely, transition bias and positional asymmetry, respectively. Repeated selection over small successive changes produces genetic codes that are substantially, but not optimally, error correcting. In particular, our model reproduces the error-correcting patterns of the standard genetic code. Aspects of our model and results may be applicable to the general problem of adaptation to error in other natural information-processing systems.     

Population Cycles,Disease, and Networks of Ecological Knowledge

Wildlife populations in the northern reaches of the globe have long been observed to fluctuate or cycle periodically, with dramatic increases followed by catastrophic crashes. Focusing on the early work of Charles S. Elton, this article analyzes how investigations into population cycles shaped the development of Anglo-American animal ecology during the 1920s–1930s. Population cycling revealed patterns that challenged ideas about the “balance” of nature; stimulated efforts to quantify population data; and brought animal ecology into conversation with intellectual debates about natural selection. Elton used the problem of understanding wildlife population cycles to explore a central tension in ecological thought: the relative influences of local conditions (food supply, predation) and universal forces (such as climate change and natural selection) in regulating wild animal populations. He also sought patronage and built research practices and the influential Bureau of Animal Population around questions of population regulation during the 1930s. Focusing on disease as a local population regulator that could interact with global climatic influences, Elton facilitated an interdisciplinary and population-based approach in early animal ecology. Elton created a network of epidemiologists, conservationists, pathologists and mathematicians, who contributed to population cycle research. I argue that, although these people often remained peripheral to ecology, their ideas shaped the young discipline. Particularly important were the concepts of abundance, density, and disease; and the interactions between these factors and natural selection. However, Elton’s reliance on density dependence unwittingly helped set up conditions conducive to the development of controversies in animal ecology in later years. While ecologists did not come to consensus on the ultimate causes of population cycles, this phenomenon was an important early catalyst for the development of theory and practice in animal ecology.     

Population history and natural selection shape patterns of genetic variation in 132 genes

Identifying regions of the human genome that have been targets of natural selection will provide important insights into human evolutionary history and may facilitate the identification of complex disease genes. Although the signature that natural selection imparts on DNA sequence variation is difficult to disentangle from the effects of neutral processes such as population demographic history, selective and demographic forces can be distinguished by analyzing multiple loci dispersed throughout the genome. We studied the molecular evolution of 132 genes by comprehensively resequencing them in 24 African-Americans and 23 European-Americans. We developed a rigorous computational approach for taking into account multiple hypothesis tests and demographic history and found that while many apparent selective events can instead be explained by demography, there is also strong evidence for positive or balancing selection at eight genes in the European-American population, but none in the African-American population. Our results suggest that the migration of modern humans out of Africa into new environments was accompanied by genetic adaptations to emergent selective forces. In addition, a region containing four contiguous genes on Chromosome 7 showed striking evidence of a recent selective sweep in European-Americans. More generally, our results have important implications for mapping genes underlying complex human diseases.     

Pathogeography: leveraging the biogeography of human infectious diseases for global health management

Biogeography is an implicit and fundamental component of almost every dimension of modern biology, from natural selection and speciation to invasive species and biodiversity management. However, biogeography has rarely been integrated into human or veterinary medicine nor routinely leveraged for global health management. Here we review the theory and application of biogeography to the research and management of human infectious diseases, an integration we refer to as ‘pathogeography’. Pathogeography represents a promising framework for understanding and decomposing the spatial distributions, diversity patterns and emergence risks of human infectious diseases into interpretable components of dynamic socio‐ecological systems. Analytical tools from biogeography are already helping to improve our understanding of individual infectious disease distributions and the processes that shape them in space and time. At higher levels of organization, biogeographical studies of diseases are rarer but increasing, improving our ability to describe and explain patterns that emerge at the level of disease communities (e.g. co‐occurrence, diversity patterns, biogeographic regionalisation). Even in a highly globalized world most human infectious diseases remain constrained in their geographic distributions by ecological barriers to the dispersal or establishment of their causal pathogens, reservoir hosts and/or vectors. These same processes underpin the spatial arrangement of other taxa, such as mammalian biodiversity, providing a strong empirical ‘prior’ with which to assess the potential distributions of infectious diseases when data on their occurrence is unavailable or limited. In the absence of quality data, generalized biogeographic patterns could provide the earliest (and in some cases the only) insights into the potential distributions of many poorly known or emerging, or as‐yet‐unknown, infectious disease risks. Encouraging more community ecologists and biogeographers to collaborate with health professionals (and vice versa) has the potential to improve our understanding of infectious disease systems and identify novel management strategies to improve local, global and planetary health.     

Evolutionary physiology at 30+: Has the promise been fulfilled?

Three decades ago, interactions between evolutionary biology and physiology gave rise to evolutionary physiology. This caused comparative physiologists to improve their research methods by incorporating evolutionary thinking. Simultaneously, evolutionary biologists began focusing more on physiological mechanisms that may help to explain constraints on and trade-offs during microevolutionary processes, as well as macroevolutionary patterns in physiological diversity. Here we argue that evolutionary physiology has yet to reach its full potential, and propose new avenues that may lead to unexpected advances. Viewing physiological adaptations in wild animals as potential solutions to human diseases offers enormous possibilities for biomedicine. New evidence of epigenetic modifications as mechanisms of phenotypic plasticity that regulate physiological traits may also arise in coming years, which may also represent an overlooked enhancer of adaptation via natural selection to explain physiological evolution. Synergistic interactions at these intersections and other areas will lead to a novel understanding of organismal biology.     

Wetland Selection by Mallard Broods in Canada's Prairie-Parklands

ABSTRACT Although brood survival has a pronounced effect on population growth in mallards (Anas platyrhynchos), knowledge of brood ecology is more limited than for other vital rates. During 1993–1997 we collected wetland selection data from 210 radiomarked mallard broods on 15 study areas located throughout the Canadian Prairie-Parklands. We used information-theoretic approaches to select the best-approximating model of habitat selection in relation to wetland characteristics. Wetland permanence, cover type, width of flooded emergent vegetation, and interactions between these variables and date, moisture level, and dominant species of emergent vegetation were all important predictors of wetland selection. Mallard broods selected deeper wetlands, especially later in the brood-rearing season. Mallard broods also selected wetlands with large central expanses of open water and wide peripheral zones of flooded emergent cover. These habitat characteristics can most easily be met in landscapes that already contain an abundance and diversity of natural wetland habitats. Where such wetlands are unavailable, restoration or management of deeper wetlands may be necessary to meet the habitat requirements of mallard ducklings.     

Using case-study based modules to promote a better understanding of evolution in an undergraduate anatomy and physiology course

ABSTRACT

The impact of evolutionary processes in understanding human health and disease is an important idea for future health professionals to understand. These students, however, typically receive little to no formal instruction in the role of evolution in not only understanding human health, but its impact on how to treat human diseases. To address this issue, we developed and implemented a case-study based learning module designed with a learning cycle implementation as part of a larger evolution across the curriculum program. The module focused on the evolution of skin color to illustrate that natural selection occurs in humans, and that the process of evolution involves tradeoffs (a balance of costs and benefits). Student understanding of the tradeoffs of natural selection was assessed through a pre- post- test design, with students answering a set of questions before instruction and again after. The modules helped improve student comprehension of natural selection, particularly for lower-performing students who were not biology majors, and for those whom reported less interest in evolution.     

Integrating selection,niche, and diversification into a hierarchical conceptual framework

Recently, new phylogenetic comparative methods have been proposed to test for the association of biological traits with diversification patterns, with species ecological “niche” being one of the most studied traits. In general, these methods implicitly assume natural selection acting at the species level, thus implying the mechanism of species selection. However, natural selection acting at the organismal level could also influence diversification patterns (i.e., effect macroevolution). Owing to our scarce knowledge on multi-level selection regarding niche as a trait, we propose a conceptual model to discuss and guide the test between species selection and effect macroevolution within a hierarchical framework. We first assume niche as an organismal as well as a species’ trait that interacts with the environment and results in species-level differential fitness. Then, we argue that niche heritability, a requirement for natural selection, can be assessed by its phylogenetic signal. Finally, we propose several predictions that can be tested in the future by disentangling both types of evolutionary processes (species selection or effect macroevolution). Our framework can have important implications for guiding analyses that aim to understand the hierarchical perspective of evolution.     

Frpm artificial individuals to global patterns

Artificial Life is a model of biological systems that describes lives archived by computer simulation, chemical substrates or any other non-biological substrates. Artificial Life simulation adopts a bottom-up approach in which behavior of lower-level entities (e.g. molecules, cells and individuals) is all that is programed; global patterns (e.g. evolutionary patterns observed at the level of the population and the community) can emerge as a result of interaction among lower-level entities. Artificial Life simulations will be used not only to test ecological and evolutionary hypotheses explaining real organisms but also to show the validity of general theories, processes and concepts such as natural selection, theories of complexity, hierarchical relations and self-organization.