Information

Learning tensors for evolutionary or developmental biology


I'm looking for book recommendations on tensor algebra for use in biology.

Tensors are being used increasingly in evolutionary biology and developmental biology, it seems. For example, here is an example of an empirical study that makes use of tensors in its analysis of data: http://rspb.royalsocietypublishing.org/content/282/1819/20151119

There are lots of books on the mathematics of tensors, and many introductions online. So far, what I have found is that either (a) the presentation is heavily oriented toward physics applications, or (b) the presentation begins from an abstract algebra (or category theory?) point of view.

I'm willing to work through physics-style or abstract algebra presentations of tensor concepts and methods, but I am wondering whether there are any textbooks or other introductions that someone would recommend for biological applications.

(I have read the brief appendix on tensors in Sean Rice's book Evolutionary Theory. Although I've found his other mathematical appendices and most of the mathematics in the book accessible, the tensor material in the book is too brief for me--I need more.)


The field of tensor decompositons has been discovered in many fields simultaneously, each with their own notation. Many modern notation in fields like CS or STAT have originated from the work of Tamara Kolda http://www.kolda.net/publication/koba09/ However, this is restricted to the CP and Tucker decomposition, and we have many others now. See https://arxiv.org/pdf/1609.00893.pdf and https://arxiv.org/abs/1708.09165


Evolutionary algorithm

In computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, [1] a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions (see also loss function). Evolution of the population then takes place after the repeated application of the above operators.

Evolutionary algorithms often perform well approximating solutions to all types of problems because they ideally do not make any assumption about the underlying fitness landscape. Techniques from evolutionary algorithms applied to the modeling of biological evolution are generally limited to explorations of microevolutionary processes and planning models based upon cellular processes. In most real applications of EAs, computational complexity is a prohibiting factor. [2] In fact, this computational complexity is due to fitness function evaluation. Fitness approximation is one of the solutions to overcome this difficulty. However, seemingly simple EA can solve often complex problems [ citation needed ] therefore, there may be no direct link between algorithm complexity and problem complexity.


Office: Bellini Life Sciences, 267 | Email: gary.brouhard [at] mcgill.ca

Cells can build an amazing variety of structures from proteins. We are interested in the biophysical mechanisms by which cells engineer these large-scale structures--in other words, the molecular basis of morphology. More specifically, we investigate the proteins that control the microtubule cytoskeleton, the backbone of cellular morphology. Our experiments combine cutting-edge microscopy, cryo-EM, stem cells, and computational models.


Obesity with associated developmental delay and/or learning disability in patients exhibiting additional features: Report of novel pathogenic copy number variants † ‡

Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Rua do Matao, 277, room 204/209, 05508-090, Sao Paulo, SP, Brazil.Search for more papers by this author

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Genetics Unit, Department of Pediatrics, Children Institute, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

Genetics Unit, Department of Pediatrics, Children Institute, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

Neurogenetics Unit, Department of Medical Genetics, School of Medicine, University of Sao Paulo, Ribeirao Preto, Brazil

Department of Morphology, Medical Genetics Center, Federal University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Rua do Matao, 277, room 204/209, 05508-090, Sao Paulo, SP, Brazil.Search for more papers by this author

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Genetics Unit, Department of Pediatrics, Children Institute, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

Genetics Unit, Department of Pediatrics, Children Institute, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

Neurogenetics Unit, Department of Medical Genetics, School of Medicine, University of Sao Paulo, Ribeirao Preto, Brazil

Department of Morphology, Medical Genetics Center, Federal University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Conflicts of interest: None.

How to Cite this Article: D'Angelo CS, Kohl I, Varela MC, de Castro CIE, Kim CA, Bertola DR, Lourenço CM, Perez ABA, Koiffmann CP. 2013. Obesity with associated developmental delay and/or learning disability in patients exhibiting additional features: Report of novel pathogenic copy number variants. Am J Med Genet Part A 161A: 479–486.


Obesity with associated developmental delay and/or learning disability in patients exhibiting additional features: Report of novel pathogenic copy number variants † ‡

Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Rua do Matao, 277, room 204/209, 05508-090, Sao Paulo, SP, Brazil.Search for more papers by this author

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Genetics Unit, Department of Pediatrics, Children Institute, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

Genetics Unit, Department of Pediatrics, Children Institute, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

Neurogenetics Unit, Department of Medical Genetics, School of Medicine, University of Sao Paulo, Ribeirao Preto, Brazil

Department of Morphology, Medical Genetics Center, Federal University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Rua do Matao, 277, room 204/209, 05508-090, Sao Paulo, SP, Brazil.Search for more papers by this author

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Genetics Unit, Department of Pediatrics, Children Institute, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

Genetics Unit, Department of Pediatrics, Children Institute, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

Neurogenetics Unit, Department of Medical Genetics, School of Medicine, University of Sao Paulo, Ribeirao Preto, Brazil

Department of Morphology, Medical Genetics Center, Federal University of Sao Paulo, Sao Paulo, Brazil

Human Genome and Stem Cell Center, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of Sao Paulo, Sao Paulo, Brazil

Conflicts of interest: None.

How to Cite this Article: D'Angelo CS, Kohl I, Varela MC, de Castro CIE, Kim CA, Bertola DR, Lourenço CM, Perez ABA, Koiffmann CP. 2013. Obesity with associated developmental delay and/or learning disability in patients exhibiting additional features: Report of novel pathogenic copy number variants. Am J Med Genet Part A 161A: 479–486.

Institutional Login
Log in to Wiley Online Library

If you have previously obtained access with your personal account, please log in.


DISCUSSION

The results of the final field test indicate the EvoDevoCI is a valid and reliable measure of student understanding of evo-devo, with test items that reference plausible biological scenarios validated by evo-devo experts. Cronbach's alpha is low for undergraduate students having taken few biology courses, and the tool has increased reliability among students who have taken five or more biology courses.

The difficulty range for items is high (0.22–0.55 lower numbers indicate more difficult items), and the overall difficulty of the CI is not significantly different for students having taken less than five biology courses, as compared with students having taken five or more biology courses (Figure 3). This latter result is disappointing, but expected, based on our previous work indicating that both novice and advanced students lack the foundational content knowledge needed to answer evo-devo questions correctly (Hiatt et al., 2013). As evo-devo instruction gains a larger foothold in mainstream biology courses, we expect student performance on the EvoDevoCI to improve. We are currently undertaking additional research to examine learning gains after specific instruction, asking whether evo-devo understanding improves when a student possesses developmental biology or other foundational knowledge.

Student Reasoning and Item Context

Nehm and Ha (2011) have identified a number of contexts that affect how students reason about evolutionary situations: plants versus animals, familiar versus unfamiliar species, gain versus loss of traits, and evolution within versus among species. These dichotomies are notable, because, while they do not usually affect how experts interpret questions, students often view the opposing contexts as fundamentally different. In the EvoDevoCI, all of the scenarios referenced animals familiar to biology majors. With regard to gains versus losses, most items in the CI reference character-state changes that are neither straightforward gains nor straightforward losses. With regard to within- versus between-species differences, the CI contains a mix of both. In a case in which a shared target concept allowed for comparison, we found the pattern observed by Nehm and Ha (2011), namely, that items referencing between-species differences are more difficult, particularly for novices. By either controlling for contexts that provoke differences in students’ reasoning (i.e., for animals vs. plants and gains vs. losses) or including a mix of both sides of the dichotomy (i.e., for within- versus between-species differences), we have attempted to minimize unwanted variation in student reasoning, while examining a diversity of potential contexts when possible.

Prevalence of Evo-Devo Conceptual Difficulties

Our previous research revealed that students often fail to master evo-devo concepts because they lack foundational concepts from developmental biology, genetics, and molecular biology (Hiatt et al., 2013). Because distracters for any particular item in the CI were based on conceptual difficulties empirically associated with the concept targeted by the item (Hiatt et al., 2013), more broadly associated conceptual difficulties have greater representation among distracters. The conceptual difficulty with the greatest representation, associated with four concepts, is “Lack of development” (DV1), followed by “Gene expression evolves only when genes appear or disappear” (ED2), which is associated with three concepts (Figure 2). In contrast, three of the conceptual difficulties associated with the concept “Mutations that are less pleiotropic are more likely to contribute to evolution” (CC3) are exclusively associated with that concept and thus are represented less among distracters.

Although advanced students did not perform significantly better on the EvoDevoCI than novice students, generally speaking, advanced students did choose specific distracters/conceptual difficulties at lower frequencies than did novice students (Table 4), in some cases, much lower (8.55% lower for DV1 in an item targeting CC2 11.4% lower for EV2 in a CC1 item and 8.95% lower for ED2 in a CC4 item). This trend is expected if indeed students overcome the conceptual difficulties associated with evo-devo concepts as they progress from novice to advanced. Exceptions to this trend identify conceptual difficulties for which current modes of instruction have either no effect or a reverse effect. For example, the percentage of students choosing the distracter “Lack of development” (DV1) in an item targeting CC5 did not change much. In the cases of “Changes in gene expression result only from mutations in said gene” (ED1) in a CC4 item and “Inheritance of acquired traits” (EV2) in a CC5 item, advanced students chose the distracters 6.1% and 4.4% more frequently than novice students, respectively.

The fact that some conceptual difficulties in understanding evolution are encountered only or more commonly among advanced students has been reported (Andrews et al., 2012). In these cases, it could be that some conceptual difficulties actually require more knowledge and are not encountered until students have some exposure to developmental biology or evo-devo. An expert is able to apply a subset of his or her knowledge to particular problems with less effort than a student (Bransford et al., 2000). In our study, however, while advanced students likely hold a larger repertoire of evo-devo content knowledge than novice students, they still seem to lack the ability to apply this knowledge to particular problems and instead may incorrectly associate more sophisticated concepts or supply factually correct but unlikely solutions. A caveat here is that our categories of “novice” and “advanced” are based merely on the number of biology courses taken and likely include students that have had an array of different course experiences. More precise data on prior concept exposure would be useful for any future studies of students’ conceptual difficulties with evo-devo.

Limitations

An instrument such as the EvoDevoCI has intrinsic limitations. For one, our goal of a short instrument that takes little class time required that our assessment be based on relatively few multiple-choice questions targeting only the most essential core concepts. This necessarily limited the breadth of the instrument, precluding the inclusion of more sophisticated evo-devo concepts that are nonetheless arguably of great evolutionary importance. These included canalization, genetic assimilation and accommodation, gene–environment interactions, epigenetic modification of DNA, gene duplication and genome evolution, serial homology, modularity, facilitated evolution, and the evolution of multicellularity. Supplementing the EvoDevoCI with 1) questions on additional topics, 2) reasoning contexts, and 3) two-tiered ( Treagust and Haslam, 1986) or open-ended questions (Nehm and Schonfeld, 2008) ought to increase breadth when assessing student understanding of evo-devo.

The utility of this particular tool lies in its ability to assess understanding of a range of evo-devo concepts, all considered vital for undergraduate biology majors, rather than exhaustively assessing a single knowledge construct. While, in theory, a maximally reliable CI would examine only a single knowledge construct, the EvoDevoCI includes items examining five distinct evo-devo concepts, all of which are interdisciplinary in nature. This predictably results in a lower Cronbach's alpha value, which is typical of similar CIs, such as the Genetics Concept Assessment (Smith et al., 2008). The construction of a CI requires balancing the reliability of the instrument to capture student understanding on the one hand with practicality and usability on the other (Adams and Wieman, 2010).

Finally, in constructing the EvoDevoCI, we have no desire to canonize any part of evolutionary developmental biology. Instead, we recognize that, as our scientific understanding of evo-devo improves, our inventory of evo-devo concepts and attendant conceptual difficulties, along with the tool we designed to assess them, must also change. Our hope is that future tools designed to assess student knowledge of evo-devo will benefit from and build upon the EvoDevoCI.

Uses for the EvoDevoCI

The EvoDevoCI is a diagnostic test designed to assess conceptual understanding of a set of core concepts in evo-devo among undergraduate biology majors. Given that the CI has been validated with a geographically and institutionally diverse student population, ranging from freshmen to seniors, the tool has different potential applications.

At RIS, faculty members currently use the EvoDevoCI pre- and postinstruction to assess the knowledge students gain from an evo-devo unit taught in upper-level courses in evolution and embryology and lower-level courses in animal biology. In these applications, the CI is taken online with a 2- or 3-wk interval between pre- and postadministrations. Similarly, faculty members at MCU have administered the CI during the first and last weeks of courses in organismal biology to assess newly implemented evo-devo instruction in these courses.

Our hope is that the EvoDevoCI can be used to complement the growing number of diagnostic instruments, allowing instructors to capture a more complete snapshot of student understanding of evolution. As per the recommendations of Vision and Change ( Bauerle et al., 2011), the EvoDevoCI marries disciplines and focuses on assessing concepts. Because of the exclusive focus on concepts, however, we advise using the EvoDevoCI in conjunction with assessments designed to assess competencies. In this way, student knowledge of both evo-devo concepts and the practices used to arrive at them can be fully assessed.


In development, it's all about the timing

Closely related organisms share most of their genes, but these similarities belie major differences in behavior, intelligence, and physical appearance. For example, we share nearly 99% of our genes with chimps, our closest relatives on the great "tree of life." Still, the differences between the two species are unmistakable. If not just genes, what else accounts for the disparities? Scientists are beginning to appreciate that the timing of the events that happen during development plays a decisive role in defining an organism, which may help to explain how species evolve without the creation of new genes.

Today, a team of scientists at Cold Spring Harbor Laboratory (CSHL) has identified a key regulator of developmental timing. Led by CSHL Assistant Professor Christopher Hammell, the researchers describe how LIN-42, a gene that is found in animals across the evolutionary tree, governs a broad range of events throughout development.

"A great deal of science is focused on understanding how a single gene functions in the cell," says Hammell. "But we are learning that when a gene is active is just as important as what it does."

An organism develops in well-defined stages: nerves and muscles mature before reproductive tissues, for example. The stages unfold sequentially, like movements in a symphony. Played all at once, they would produce a terrible developmental cacophony, but with proper timing, a natural harmony can emerge.

Developmental stages are marked by the activation or repression of a specific and unique complement of genes, like individual notes within movements of a song. The order and duration of when these key developmental genes (or notes) are active (or played) within a given cell is controlled by a class of molecules called microRNAs (miRNAs). A single miRNA gene can control hundreds of other genes at once. If a miRNA turns off these specific genes too early or too late, the organism will suffer severe developmental defects. But little is known about how the activities of these miRNAs are regulated.

In work published in PLoS Genetics, Hammell and his team describe the genetic approach they used to search for genes that control developmental timing through miRNAs. The team uses a tiny roundworm, called C. elegans, as a simple model for the events that occur during development, even in higher organisms. These worms have a fixed number of cells and each cell division is precisely timed. "It is the perfect model for our work," says Hammell. "It enables us to understand exactly how a mutation affects development, whether maturation is precocious or delayed, by directly observing defects in the timing of gene expression."

The team's search uncovered the gene LIN-42 as a crucial regulator of developmental timing via its pervasive role in controlling miRNAs. "LIN-42 shares a significant amount of similarity to the genes that control circadian rhythms in organisms such as mice and humans," says Roberto Perales, PhD, one of the lead authors of the study. "These are genes that control the timing of cellular processes on a daily basis for you and me. In the worm, these same genes and mechanisms control development, growth, and behavior and this system will provide us with leverage to understand how all of these things are coordinated."

Hammell and his team found that LIN-42 controls the repression of numerous genes in addition to miRNAs. They also discovered that levels of the protein encoded by LIN-42 tend to oscillate over the course of development and form a part of a developmental clock. "LIN-42 provides the organism with a kind of cadence or temporal memory, so that it can remember that it has completed one developmental step before it moves on to the next," says Hammell. "This way, LIN-42 coordinates optimal levels of the genes required throughout development."


EDITORIAL article

The computational theory of mind, which views the brain as an information processor that operates on cognitive representations, is central to modern cognitive psychology and is the dominant perspective from which brain function is conceptualized and studied. Evolutionary Psychology (EP) is the application of evolutionary theory to understanding human behavior and cognition. Unlike other core Psychology topic areas (such as Personality, Learning, or Developmental Psychology), however, EP is not defined by the subset of psychological phenomena it seeks to describe and understand. It is instead defined by a specific meta-theoretical perspective, from which it seeks to (potentially) explain all psychological phenomena. The central question posed by this volume is whether this over-arching nature provides an opportunity for evolutionary approaches to offer an alternative meta-theoretical perspective to the information processing/representational view of brain function and behavior.

Readers of this volume will notice a sharp demarcation between descriptions of traditional Evolutionary Psychology, which several authors (Barret et al. Stotz Stulp et al.) have presented as indistinguishable from the information processing approach, and newer conceptualizations of EP. Indeed one of the major themes running through several of the contributions (Burke Barret et al. Stephen Stotz Stulp et al.) concerns the appropriate conceptualization of EP itself, with the Santa Barbara school of massive modularity (made famous by John Tooby and Leda Cosmides) receiving the most scrutiny. As Barret et al. and Stotz describe, early conceptualizations of EP embraced the notion of massive modularity of mind. Individual modules were presumed to act as evolved computers, sensitive to domain specific information and processing it in adaptive ways. Framed in this manner, EP fits well within even a very strict definition of a computational theory of mind and could hardly be seen as the source of an alternative meta-theoretical approach to understanding brain and behavior.

It may not be appropriate, however, to view either the computational theory of mind or the field of EP so narrowly. As Klasios argues, many evolutionary psychologists adopt a more generic notion of computation, one that commits more to the abstract representation and manipulation of information, rather than to digital computation in its literal sense (although see also Bryant). EP too, is no longer wed to notions of massive modularity (Stephen), with the majority of research in the field motivated by consideration of first principles of evolutionary theory and is neither constrained nor informed by assumptions of massive modularity or domain specific mechanisms (Burke). With these considerations in mind, Klasios and Bryant both argue that computation is still the most profitable account of the mind and is able to accommodate both evolutionary and e-cognition (extended, embodied approaches which place emphasis on the role played by the whole organism and its environment in the decision-making process, rather than simply the brain) perspectives, that favor notions of neural adaptations that are 𠇌omplex, widely distributed, and highly diffuse” (Klasios) over the more strictly isolated mental modules supposed by massive modularity.

Burke further argues that commitments to massive modularity, or to either a computational, direct, or e-cognition view of the brain, are unnecessary for evolutionary theory to become the foundational theory of psychological science. Presenting a series of six reasons for the current failure of evolutionary theory to inform most research within psychological science, Burke (with supporting arguments given by Jonason and Dane, and Stephen), suggests that a mixture of misunderstandings about the field of EP coupled with motivated opposition and misguided skepticism are to blame.

If Burke’s assessment is accurate, such barriers may only be overcome by a concerted effort to unite EP with Behavioral Ecology and Evolutionary Biology. Stotz proposes one such unity with her Extended Evolutionary Psychology. Combining evolutionary theories concerning genetic, epigenetic, behavioral, and cultural systems of inheritance, developmental plasticity and niche construction, with e-cognition, Stotz outlines a truly integrative EP. Stotz’ Extended Evolutionary Psychology draws on complex mechanisms of inheritance to help understand the evolution of psychological traits. But it also sees investigations of e-cognition informing theories of niche construction and transgenerational developmental plasticity. Thus, the integration of evolutionary theory with psychology provides reciprocated benefits to both fields.

Barrett et al. Barrett et al. and Stulp et al. argue for an Extended Mind Hypothesis. The Extended Mind Hypothesis sits within an evolutionarily informed framework, but places much emphasis on the sociocultural nature of human psychology and the external resources (cultural and technological artifacts) that form part of the modern human cognitive system. The Extended Mind Hypothesis offers the various forms of e-cognition, rather than EP, as the appropriate meta-theoretical perspective to succeed the computational theory of mind. In arguments that mirror those presented by Burke, however, Stephen et al. argue that while e-cognition represents an interesting alternative to more traditional proximal explanations of behavior (such as computational theory of mind), behavior must still be examined through a sophisticated evolutionary lens if an ultimate understanding is to be reached.

Newer conceptualizations of EP are uncommitted to notions of massive modularity, look beyond the Pleistocene for the selection pressures that have shaped psychological mechanisms and incorporate developmental and cultural impacts into theories concerning the evolved functions of psychological mechanisms. It is clear however, that the massive modularity roots of modern EP still influence how many, including both advocates and critics, view the field. One message that is clear from the works presented in this volume, is that EP must mature and free itself of many of its early assumptions and assertions (as seems to be currently happening empirically, if not yet theoretically, Burke). Only if this occurs, will EP be placed to properly integrate with Evolutionary Biology and be in a position to cement evolutionary theory as a unifying meta-theory for Psychological Science. Whether such a New Evolutionary Psychology should incorporate computational theories of mind or reject these in favor of the newer e-cognition perspectives is an empirical question and not one whose answer needs to be decided before the weight of evidence has settled in either court (Stephen).


Published by the Royal Society. All rights reserved.

References

. 1978 The methodology of scientific research programmes . Cambridge, UK : Cambridge University Press . Crossref, Google Scholar

. 1979 The genesis and development of a scientific fact . Chicago, IL : Chicago University Press . Google Scholar

. 1962 The structure of scientific revolutions . Chicago, IL : Chicago University Press . Google Scholar

. 1971 The origins of theoretical population genetics . Chicago, IL : Chicago University Press . Google Scholar

. 1982 The growth of biological thought: diversity, evolution and inheritance . Cambridge, MA: Belknap Press . Google Scholar

. 1998 Evolutionary biology . Sunderland, MA : Sinauer . Google Scholar

. 2014 Phenotypic evolution: the ongoing synthesis . Am. Nat . 1836, 729–746. (doi:10.1086/675304) Crossref, Google Scholar

. 2014 From gene action to reactive genomes . J. Physiol . 592, 2423–2429. (doi:10.1113/jphysiol.2014.270991) Crossref, PubMed, ISI, Google Scholar

. 2013 Genetics and philosophy. An introduction . Cambridge, UK : Cambridge University Press . Crossref, Google Scholar

Wray GA, Hoekstra HE, Futuyma DJ, Lenski RE, Mackay TFC, Schluter D, Strassman JE

. 2014 Does evolutionary theory need a rethink? No, all is well . Nature 514, 161–164. (doi:10.1038/514161a) Crossref, PubMed, ISI, Google Scholar

Gilbert SF, Opitz J, Raff RA

. 1996 Resynthesizing evolutionary and developmental biology . Dev. Biol . 173, 357–372. (doi:10.1006/dbio.1996.0032) Crossref, PubMed, ISI, Google Scholar

. 2002 The structure of evolutionary theory . Cambridge, MA : Belknap Press . Crossref, Google Scholar

(eds). 2010 Evolution: the extended synthesis . Cambridge, MA : MIT Press . Crossref, Google Scholar

Noble D, Jablonka E, Joyner M, Müller GB, Omholt S

. 2014 The integration of evolutionary biology with physiological science . J. Physiol . 592, 2237–2244. (doi:10.1113/jphysiol.2014.273151) Crossref, PubMed, Google Scholar

et al. 2014 Does evolutionary theory need a rethink? Nature 514, 161–164. Crossref, PubMed, ISI, Google Scholar

. 2006 Gene regulatory networks and the evolution of animal body parts . Science 311, 796–800. (doi:10.1126/science.1113832) Crossref, PubMed, Google Scholar

. 2006 Evo-devo and constraints on selection . Trends Ecol. Evol . 21, 362–368. (doi:10.1016/j.tree.2006.05.001) Crossref, PubMed, ISI, Google Scholar

. 2011 Evo-devo and accounting for Darwin's endless forms . Phil. Trans. R. Soc. B 366, 2069–2075. (doi:10.1098/rstb.2011.0007) Link, ISI, Google Scholar

. 2007 Evo-devo: extending the evolutionary synthesis . Nat. Rev. Genet . 8, 943–950. (doi:10.1038/nrg2219) Crossref, PubMed, Google Scholar

. 1987 Developmental quantitative genetics and the evolution of ontogenies . Evolution 41, 316–330. (doi:10.2307/2409141) Crossref, PubMed, Google Scholar

. 2014 Epigenetic processes and genetic architecture . In Quantitative genetics in the wild (eds

Charmantier WA, Garant D, Kruuk LEB

), pp. 177–189. Oxford, UK : Oxford University Press . Crossref, Google Scholar

. 1996 Perspective: complex adaptations and the evolution of evolvability . Evolution 50, 967–976. (doi:10.2307/2410639) Crossref, PubMed, ISI, Google Scholar

. 2007 What is evolvability? In Philosophy of biology (eds

), pp. 163–178. Amsterdam, The Netherlands : Elsevier . Crossref, Google Scholar

. 2010 Evolution of evolvability In Evolution: the extended synthesis (eds

), pp. 218–228. Cambridge, MA : MIT Press . Google Scholar

. 2004 Biased embryos and evolution . Cambridge, UK : Cambridge University Press . Crossref, Google Scholar

. 2011 Evolution: a developmental approach . Oxford, UK: Wiley-Blackwell . Google Scholar

. 2003 Developmental plasticity and evolution . Oxford, UK : Oxford University Press . Crossref, Google Scholar

. 2005 The plausibility of life: resolving Darwin's Dilemma . New Haven, CT : Yale University Press . Google Scholar

Galis F, Arntzen JW, Lande R

. 2010 Dollo's law and the irreversibility of digit loss in Bachia . Evolution 64, 2466–2476. (doi:10.1111/j.1558-5646.2010.01041.x) PubMed, ISI, Google Scholar

Lange A, Nemeschkal HL, Müller GB

. 2014 Biased polyphenism in polydactylous cats carrying a single point mutation: the Hemingway model for digit novelty . Evol. Biol . 41, 262–275. (doi:10.1007/s11692-013-9267-y) Crossref, Google Scholar

Beldade P, Koops K, Brakefield PM

. 2002 Developmental constraints versus flexibility in morphological evolution . Nature 416, 844–847. (doi:10.1038/416844a) Crossref, PubMed, ISI, Google Scholar

Shubin N, Tabin C, Carroll S

. 2009 Deep homology and the origins of evolutionary novelty . Nature 457, 818–823. (doi:10.1038/nature07891) Crossref, PubMed, ISI, Google Scholar

. 2006 Genetic and developmental basis of cichlid trophic diversity . Heredity 97, 211–221. (doi:10.1038/sj.hdy.6800864) Crossref, PubMed, Google Scholar

. 2007 The theory of facilitated variation . Proc. Natl Acad. Sci. USA 104, 8582–8589. (doi:10.1073/pnas.0701035104) Crossref, PubMed, ISI, Google Scholar

. 1968 Evolution in changing environments . Princeton, NJ : Princeton University Press . Crossref, Google Scholar

. 1985 Genotype–environment interaction and the evolution of phenotypic plasticity . Evolution 39, 505–522. (doi:10.2307/2408649) Crossref, PubMed, ISI, Google Scholar

. 1986 The evolution of phenotypic plasticity in plants . Annu. Rev. Ecol. Syst . 17, 667–693. (doi:10.1146/annurev.es.17.110186.003315) Crossref, Google Scholar

. 1989 The evolutionary significance of phenotypic plasticity . Bioscience 39, 436–445. (doi:10.2307/1311135) Crossref, ISI, Google Scholar

. 2001 Phenotypic plasticity: beyond nature and nurture . Baltimore, MD : John Hopkins University Press . Google Scholar

. 2004 Adaptive phenotypic plasticity and the successful colonization of a novel environment . Am. Nat . 164, 531–542. (doi:10.1086/423825) Crossref, PubMed, ISI, Google Scholar

Sol D, Duncan RP, Blackburn TM, Cassey P, Lefebvre L

. 2005 Big brains, enhanced cognition, and response of birds to novel environments . Proc. Natl Acad. Sci. USA 102, 5460–5465. (doi:10.1073/pnas.0408145102) Crossref, PubMed, ISI, Google Scholar

. 2008 Modifying effects of phenotypic plasticity on interactions among natural selection, adaptation and gene flow . J. Evol. Biol . 21, 1460–1469. (doi:10.1111/j.1420-9101.2008.01592.x) Crossref, PubMed, ISI, Google Scholar

Huey RB, Hertz PE, Sinervo B

. 2003 Behavioral drive versus behavioral inertia in evolution: a null model approach . Am. Nat . 161, 357–366. (doi:10.1086/346135) Crossref, PubMed, ISI, Google Scholar

. 2009 The role of behavior in evolution . Evol. Ecol . 23, 513–531. (doi:10.1007/s10682-008-9252-6) Crossref, Google Scholar

. 2010 Towards an evolutionary ecology of sexual traits . Trends Ecol. Evol . 25, 145–152. (doi:10.1016/j.tree.2009.09.008) Crossref, PubMed, ISI, Google Scholar

Price TD, Qvarnström A, Irwin DE

. 2003 The role of phenotypic plasticity in driving genetic evolution . Proc. R. Soc. Lond. B 270, 1433–1440. (doi:10.1098/rspb.2003.2372) Link, ISI, Google Scholar

. 2009 Adaptation to an extraordinary environment by evolution of phenotypic plasticity and genetic assimilation . J. Evol. Biol . 22, 1435–1446. (doi:10.1111/j.1420-9101.2009.01754.x) Crossref, PubMed, ISI, Google Scholar

. 2010 Resource polyphenism increases species richness: a test of the hypothesis . Phil. Trans. R. Soc. B 365, 577–591. (doi:10.1098/rstb.2009.0244) Link, ISI, Google Scholar

2011 The role of developmental plasticity in evolutionary innovation . Proc. R. Soc. B 278, 2705–2713. (doi:10.1098/rspb.2011.0971) Link, ISI, Google Scholar

. 1902 Development and evolution . New York, NY : MacMillan . Google Scholar

. 1949 Factors of evolution: the theory of stabilizing selection . Toronto, Canada : Blakiston . Google Scholar

Schlichting CD, Pigliucci M

. 1998 Phenotypic evolution: a reaction norm perspective . Sunderland, MA : Sinauer . Google Scholar

. 2010 When do adaptive plasticity and genetic evolution prevent extinction of a density-regulated population? Evolution 64, 1143–1150. (doi:10.1111/j.1558-5646.2009.00875.x) Crossref, PubMed, ISI, Google Scholar

. 2006 Evolution of a polyphenism by genetic accommodation . Science 311, 650–652. (doi:10.1126/science.1118888) Crossref, PubMed, ISI, Google Scholar

Wund MA, Baker JA, Clancy B, Golub JL, Foster SA

. 2008 A test of the ‘Flexible stem’ model of evolution: ancestral plasticity, genetic accommodation, and morphological divergence in the threespine stickleback radiation . Am. Nat . 172, 449–462. (doi:10.1086/590966) Crossref, PubMed, ISI, Google Scholar

2010 Phenotypic plasticity's impacts on diversification and speciation . Trends Ecol. Evol . 25, 459–467. (doi:10.1016/j.tree.2010.05.006) Crossref, PubMed, ISI, Google Scholar

Standen EM, Du TY, Larsson HCE

. 2014 Developmental plasticity and the origin of tetrapods . Nature 513, 54–58. (doi:10.1038/nature13708) Crossref, PubMed, ISI, Google Scholar

. 2014 Phenotypic plasticity and epigenetic marking: an assessment of evidence for genetic accommodation . Evolution 68, 656–672. (doi:10.1111/evo.12348) Crossref, PubMed, ISI, Google Scholar

Cavalli-Sforza LL, Feldman MW

. 1981 Cultural transmission and evolution . Princeton, NJ : University of Princeton Press . Google Scholar

. 2014 Evolution in four dimensions , 2nd edn. Cambridge, MA : MIT Press . Google Scholar

2011 Beyond DNA: integrating inclusive inheritance into an extended theory of evolution . Nat. Rev. Genet . 12, 475–486. (doi:10.1038/nrg3028) Crossref, PubMed, ISI, Google Scholar

. 1987 Settling nature and nurture into an ontogenetic niche . Dev. Psychobiol . 20, 549–562. (doi:10.1002/dev.420200508) Crossref, PubMed, Google Scholar

. 2009 Parental effects in ecology and evolution: mechanisms, processes and implications . Phil. Trans. R. Soc. B 364, 1169–1177. (doi:10.1098/rstb.2008.0302) Link, ISI, Google Scholar

. 2009 Ecological developmental biology: integrating epigenetics, medicine, and evolution . Sunderland, MA: Sinauer Associates . Google Scholar

. In press. Heredity and evolution . In Challenges to evolutionary theory: development and heredity (eds

). Oxford, UK : Oxford University Press . Google Scholar

. 2013 Social learning: an introduction to mechanisms, methods, and models . Princeton, NJ : Princeton University Press . Crossref, Google Scholar

. 2012 Parental effects in development and evolution . In Evolution of parental care (eds

Royle NJ, Smiseth PT, Kölliker M

), p. 376. Oxford, UK : Oxford University Press . Google Scholar

. 2009 Evolutionary significance of phenotypic accommodation in novel environments: an empirical test of the Baldwin effect . Phil. Trans. R. Soc. B 364, 1125–1141. (doi:10.1098/rstb.2008.0285) Link, ISI, Google Scholar

. 2014 Transgenerational epigenetic inheritance: myths and mechanisms . Cell 157, 95–109. (doi:10.1016/j.cell.2014.02.045) Crossref, PubMed, ISI, Google Scholar

van der Graaf A, Wardenaar R, Neumann DA, Taudt A, Shaw RG, Jansen RC, Schmitz RJ, Colome-Tatche M, Johannes F

. 2015 Rate, spectrum, and evolutionary dynamics of spontaneous epimutations . Proc. Natl Acad. Sci. USA 112, 6676–6681. (doi:10.1073/pnas.1424254112) Crossref, PubMed, ISI, Google Scholar

. 1983 Gene, organism and environment . In Evolution from molecules to men (ed.

), pp. 273–285. Cambridge, UK : Cambridge University Press . Google Scholar

Odling-Smee FJ, Laland KN, Feldman MW

. 1996 Niche construction . Am. Nat . 147, 641–648. (doi:10.1086/285870) Crossref, ISI, Google Scholar

Odling-Smee FJ, Laland KN, Feldman MW

. 2003 Niche construction: the neglected process in evolution. Monographs in population biology , 37. Princeton, UK : Princeton University Press . Google Scholar

. 2010 Niche inheritance . In Evolution: the extended synthesis (eds

), pp. 175–208. Cambridge, MA : MIT Press . Google Scholar

. 2014 Why ontogeny matters during adaption: developmental niche construction and pleiotropy across the life cycle in Arabidopsis thaliana . Evolution 68, 32–47. (doi:10.1111/evo.12284) Crossref, PubMed, Google Scholar

. 2008 Macroevolution of ecosystem engineering, niche construction and diversity . Trends Ecol. Evol . 23, 304–310. (doi:10.1016/j.tree.2008.01.013) Crossref, PubMed, Google Scholar

Odling-Smee FJ, Erwin D, Palkovacs E, Feldman M, Laland KN

. 2013 Niche construction theory: a practical guide for ecologists . Q. Rev. Biol . 88, 3–28. (doi:10.1086/669266) Crossref, ISI, Google Scholar

Krakauer DC, Page KM, Erwin DH

. 2009 Diversity, dilemmas, and monopolies of niche construction . Am. Nat . 173, 26–40. (doi:10.1086/593707) Crossref, PubMed, Google Scholar

Laland KN, Odling-Smee FJ, Feldman MW

. 1996 On the evolutionary consequences of niche construction . J. Evol. Biol . 9, 293–316. (doi:10.1046/j.1420-9101.1996.9030293.x) Crossref, Google Scholar

Laland KN, Odling-Smee FJ, Feldman MW

. 1999 Evolutionary consequences of niche construction and their implications for ecology . Proc. Natl Acad. Sci. USA 96, 10 242–10 247. (doi:10.1073/pnas.96.18.10242) Crossref, ISI, Google Scholar

Kerr B, Schwilk DW, Bergman A, Feldman MW

. 1999 Rekindling an old flame: a haploid model for the evolution and impact of flammability in resprouting plants . Evol. Ecol. Res . 1, 807–833. Google Scholar

Creanza N, Fogarty L, Feldman MW

. 2012 Models of cultural niche construction with selection and assertive mating . PLoS ONE 7, e42744. (doi:10.1371/journal.pone.0042744) Crossref, PubMed, Google Scholar

. 2006 Spatial effects favour the evolution of niche construction . Theor. Pop. Biol . 70, 387–400. (doi:10.1016/j.tpb.2006.08.003) Crossref, PubMed, Google Scholar

. 2008 The adaptive dynamics of niche constructing traits in spatially subdivided populations: evolving posthumous extended phenotypes . Evolution 62, 549–566. (doi:10.1111/j.1558-5646.2007.00291.x) Crossref, PubMed, ISI, Google Scholar

. 2012 Origins of altruism diversity II: runaway coevolution of altruistic strategies via ‘reciprocal niche construction’ . Evolution 66, 2498–2513. (doi:10.1111/j.1558-5646.2012.01629.x) Crossref, PubMed, Google Scholar

. 2008 Ecological and evolutionary consequences of niche construction for its agent . Ecol. Lett . 11, 1072–1081. (doi:10.1111/j.1461-0248.2008.01220.x) Crossref, PubMed, Google Scholar

. 1982 The extended phenotype . Oxford, UK : Oxford University Press . Google Scholar

Scott-Phillips TC, Laland KN, Shuker DM, Dickins TE, West SA

. 2013 The niche construction perspective. A critical appraisal . Evolution 68, 1231–1243. (doi:10.1111/evo.12332) Crossref, Google Scholar

. 1961 Cause and effect in biology . Science 134, 1501–1506. (doi:10.1126/science.134.3489.1501) Crossref, PubMed, ISI, Google Scholar

Laland KN, Sterelny K, Odling-Smee FJ, Hoppitt W, Uller T

. 2011 Cause and effect in biology revisited: is Mayr's proximate–ultimate dichotomy still useful? Science 334, 1512–1516. (doi:10.1126/science.1210879) Crossref, PubMed, Google Scholar

. 2004 Phenotypic plasticity: functional and conceptual approaches . Oxford, UK : Oxford University Press . Google Scholar

. 2005 Evolution of phenotypic plasticity: patterns of plasticity and the emergence of ecotypes . New Phytol . 166, 101–117. (doi:10.1111/j.1469-8137.2005.01322.x) Crossref, PubMed, ISI, Google Scholar

. 2012 The extended evolutionary synthesis and the role of soft inheritance in evolution . Proc. R. Soc. B 278, 1721–1727. (doi:10.1098/rspb.2010.1726) Google Scholar

. 1986 Natural selection in the wild . Princeton, NJ : Princeton University Press . Google Scholar

. 2013 Evolution . Sunderland, MA : Sinauer . Google Scholar

2007 Evolution . Cold Spring Harbor, NY : Cold Spring Harbor . Google Scholar

. 2004 Evolution , 3rd edn. Cambridge, MA : Blackwell . Google Scholar

. 1970 The units of selection . Annu. Rev. Ecol. Syst . 1, 1–18. (doi:10.1146/annurev.es.01.110170.000245) Crossref, Google Scholar

. 1859 The origin of species . London, UK : John Murray Press . Google Scholar

. 2013 New thinking about biological evolution . Biol. J. Linn. Soc . 112, 268–275. (doi:10.1111/bij.12125) Crossref, Google Scholar

. 2011 Plasticity, robustness, development and evolution . Cambridge, UK : Cambridge University Press . Crossref, Google Scholar

. 1969 Paradigm for an evolutionary process . In Towards a theoretical biology (ed.

), pp. 106–124. Edinburgh, UK : Edinburgh University Press . Google Scholar

Oyama S, Griffiths PE, Gray RD

(eds). 2001 Cycles of contingency: developmental systems and evolution . Cambridge, MA : MIT Press . Google Scholar

. 2006 The music of life . Oxford, UK : Oxford University Press . Google Scholar

. 2011 Epigenetics. Linking genotype and phenotype in development and evolution . Berkeley, CA : University of California Press . Google Scholar

. 2012 Physico-genetic determinants in the evolution of development . Science 338, 217–219. (doi:10.1126/science.1222003) Crossref, PubMed, Google Scholar

. 2012 Selective processes in development: implications for the costs and benefits of phenotypic plasticity . Integr. Comp. Biol . 52, 31–42. (doi:10.1093/icb/ics067) Crossref, PubMed, Google Scholar

. 1984 The triumph of the evolutionary synthesis . Times Lit . 2, 1261–1262. Google Scholar

. 2012 The nature of nurture and the future of evodevo: toward a comprehensive theory of developmental evolution . Integrative Comp. Biol . 52, 108–119. (doi:10.1093/icb/ics048) Crossref, PubMed, Google Scholar

Laland KN, Odling-Smee J, Hoppitt W, Uller T

. 2013 More on how and why: cause and effect in biology revisited . Biol. Phil . 28, 719–745. (doi:10.1007/s10539-102-9335-1) Crossref, Google Scholar

Laland KN, Odling-Smee J, Hoppitt W, Uller T

. 2013 More on how and why: a response to commentaries . Biol. Phil . 28, 793–810. (doi:10.1007/s10539-013-9380-4) Crossref, PubMed, Google Scholar

. 2003 The morphogenesis of evolutionary developmental biology . Int. J. Dev. Biol . 47, 467–477. PubMed, ISI, Google Scholar

. 1996 Complexity and the function of mind in nature . Cambridge, UK : Cambridge University Press . Crossref, Google Scholar

. 1986 The newer synthesis? Some conceptual problems in evolutionary biology . Oxford Surv. Evol. Biol . 3, 224–243. Google Scholar

. 2011 Origin of the fittest: link between emergent variation and evolutionary change as a critical question in evolutionary biology . Proc. R. Soc. B 278, 1921–1929. (doi:10.1098/rspb.2011.0548) Link, Google Scholar

. 1871 On the genesis of species . London, UK : Macmillan and Company . Crossref, Google Scholar

. 1894 Materials for the study of variation: treated with especial regard to discontinuity in the origin of species . London, UK : Macmillan and Company . Google Scholar

. 1981 A multiple-level model of evolution and its implications for sociobiology . Behav. Brain Sci . 4, 225–268. (doi:10.1017/S0140525X00008566) Crossref, Google Scholar

. 2013 Mutation driven evolution . Oxford, UK : Oxford University Press . Google Scholar

. 1999 On the possibility of constructive neutral evolution . J. Mol. Evol . 49, 169–181. (doi:10.1007/PL00006540) Crossref, PubMed, ISI, Google Scholar

. 2012 Constructive neutral evolution: exploring evolutionary theory's curious disconnect . Biol. Direct 7, 35. (doi:10.1186/1745-6150-7-35) Crossref, PubMed, ISI, Google Scholar

. 1930 The genetical theory of natural selection . Oxford, UK : Clarendon Press . Crossref, Google Scholar

2010 Adaptive evolution of pelvic reduction in sticklebacks by recurrent deletion of a Pitx 1 enhancer . Science 327, 302–305. (doi:10.1126/science.1182213) Crossref, PubMed, Google Scholar

. 2010 Epigenetic innovation . In Evolution: the extended synthesis (eds

), pp. 307–332. Cambridge, MA : MIT Press . Google Scholar

Charlesworth B, Lande R, Slatkin M

. 1982 A neo-Darwinian commentary on macroevolution . Evolution 36, 474–498. (doi:10.2307/2408095) Crossref, PubMed, Google Scholar

. 1986 The blind watchmaker . New York, NY : Norton . Google Scholar

. 2000 The extended organism: the physiology of animal-built structures . Cambridge, MA : Harvard University Press . Google Scholar

Charlesworth B, Charlesworth D

. 2010 Elements of evolutionary genetics . Greenwood Village, CO : Roberts and Company Publishers . Google Scholar

. 1994 Developmental systems and evolutionary explanation . J. Phil . 91, 277–304. (doi:10.2307/2940982) Crossref, ISI, Google Scholar

. 1985 The ontology of information: developmental systems and evolution . Cambridge, UK : Cambridge University Press . Google Scholar

. 2014 Homology, genes, and evolutionary innovation . Princeton, NJ : Princeton University Press . Google Scholar

. 2010 Phenotypic plasticity facilitates recurrent rapid adaptation to introduced predators . Proc. Natl Acad. Sci. USA 107, 4260–4263. (doi:10.1073/pnas.0912748107) Crossref, PubMed, ISI, Google Scholar

. 2011 Epigenetic contributions to adaptive radiation. Insights from threespine stickleback . In Epigenetics: linking genotype and phenotype in development and evolution (eds

), pp. 317–336. Berkeley and Los Angeles, CA : University of California Press . Google Scholar

. 1984 The nature of selection . Cambridge, MA : MIT Press . Google Scholar

. 2005 The innovation triad: an EvoDevo agenda . J. Exp. Zool . 304, 487–503. (doi:10.1002/jez.b.21081) Crossref, Google Scholar

. 2008 On the origins of novelty in development and evolution . Bioessays 30, 432–447. (doi:10.1002/bies.20754) Crossref, PubMed, ISI, Google Scholar

Hallgrimsson B, Jamniczky HA, Young NM, Rolian C, Schmidt-Ott U, Marcucio RS

. 2012 The generation of variation and the developmental basis for evolutionary novelty . J. Exp. Zool. B Mol. Dev. Evol . 318, 501–517. (doi:10.1002/jez.b.22448) Crossref, PubMed, Google Scholar

. 1985 Culture and the evolutionary process . Chicago, IL : University of Chicago Press . Google Scholar

. 2009 Nongenetic inheritance and its evolutionary implications . Annu. Rev. Ecol. Evol. Syst . 40, 103–125. (doi:10.1146/annurev.ecolsys.39.110707.173441) Crossref, ISI, Google Scholar

. 2014 Evolutionary perspectives on transgenerational epigenetics . In Transgenerational epigenetics: evidence and debate (ed.

. 2013 The Cambrian explosion: the construction of animal biodiversity . Greenwood Village, CO : Roberts and Company . Google Scholar

Andersen H, Barker P, Chen X

. 2006 The cognitive structure of scientific revolutions . New York, NY : Cambridge University Press . Crossref, Google Scholar

. 2007 Development in context: the timely emergence of eco-devo . Trends Ecol. Evol . 22, 575–582. (doi:10.1016/j.tree.2007.06.014) Crossref, PubMed, ISI, Google Scholar

Matthews B, De Meester L, Jones CG, Iberlings BW, Bouma TJ, Nuutinen V, van der Koppel J, Odling-Smee FJ

. 2014 Under niche construction: an operational bridge between ecology, evolution, and ecosystem science . Ecol. Monogr . 84, 245–263. (doi:10.1890/13-0953.1) Crossref, Google Scholar

. 2007 Human niche construction and the behavioral context of plant and animal domestication . Evol. Anthropol . 16, 188–199. (doi:10.1002/evan.20135) Crossref, Google Scholar

. 2009 Adam's Tongue. How humans made language, how language made humans . New York, NY : Hill and Wang . Google Scholar

Kendal J, Tehrani JJ, Odling-Smee FJ

. 2011 Human niche construction in interdisciplinary focus . Phil. Trans. R. Soc. B 366, 785–792. (doi:10.1098/rstb.2010.0306) Link, ISI, Google Scholar

. 2012 Genes, culture and agriculture: an example of human niche construction . Curr. Anthropol . 53, 434–470. (doi:10.1086/666585) Crossref, Google Scholar

(eds). 2011 Cultural niche construction. Special edition of biological theory , vol. 6. Berlin, Germany : Springer Science and Business Media . Google Scholar

Flynn E, Laland KN, Kendal R, Kendal J

. 2013 Developmental niche construction . Dev. Sci . 16, 296–313. (doi:10.1111/desc.12030) Crossref, PubMed, Google Scholar

. 2009 Darwinian evolution in the light of genomics . Nucleic Acids Res. 37, 1011–1034. (doi:10.1093/nar/gkp089) Crossref, PubMed, ISI, Google Scholar

. 2011 Evolution: a view from the 21st century . Upper Saddle River, NJ : FT Press Science . Google Scholar

Watson RA, Wagner GP, Pavlicev M, Weinreich DM, Mills R

. 2014 The evolution of phenotypic correlations and ‘developmental memory’ . Evolution 68, 1124–1138. (doi:10.1111/evo.12337) Crossref, PubMed, ISI, Google Scholar


Biologists Call For Better Choice Of Model Organisms In 'Evo-devo'

Research in evolutionary developmental biology, known as &lsquoevo-devo&rsquo, is being held back because the dominant model organisms used by scientists are unable to illustrate key questions about evolution, argue biologists in the latest issue of Nature Reviews Genetics.

The subject of evo-devo, which became established almost a decade ago, is particularly dependent on the six main model organisms that have been inherited from developmental biology (fruit fly, nematode worm, frog, zebrafish, chick and mouse).

To help understand how developmental change underpins evolution, evo-devo researchers have, over recent years, selected dozens of new model organisms, ranging from sea anemones to dung beetles, to study.

One of the selection criteria deemed most crucial is the phylogenetic position of prospective model organisms, which reflects their evolutionary relationships.

Phylogenetic position is employed in two common, but problematic, ways, either as a guide to plug holes in unexplored regions of the phylogenetic tree, or as a pointer to species with presumed primitive (ancestral) characteristics.

Drs Ronald Jenner and Matthew Wills from the Department of Biology & Biochemistry at the University of Bath (UK), call for a more judicious approach to selecting organisms, based on the evo-devo themes that the organism can shed light on.

&ldquoIt is fair to say that, since its inception, some workers feel that evo-devo hasn&rsquot quite lived up to its early expectations,&rdquo said Dr Jenner.

&ldquoPartly this is because too much was expected too soon, but we suspect that in terms of its future promise the current choice of new model organisms has not yet been optimised.

Dr Wills said: &ldquoMany models to date, in particular the big six, have been chosen because they are easy to keep in the laboratory, select and breed.

&ldquoWhilst this is generally fine in the context of development research, the benefits to evo-devo as a subject are limited.

&ldquoThere are upwards of 35 phyla of animals, and four of our six best models come from just one phylum.

&ldquoHowever, that doesn&rsquot mean that simply choosing new models to plug holes in the phylogenetic tree is the best option for further progress in evo-devo.&rdquo

Dr Jenner added: &ldquoThe popular advice of choosing new model organisms to maximise phylogenetic spread is nice to show diversity, but it doesn&rsquot necessarily lead to new general insights about evolution.

&ldquoChoosing new models in this way leaves it entirely a matter of chance whether a new model will illuminate a particular evo-devo theme.

&ldquoInstead, we urge workers to select new models specifically to illuminate hitherto neglected general themes within evo-devo.&rdquo.

In other cases, new model organisms are chosen on the basis of how well they are thought to represent a particular ancestral organism. In connection to this practice, the researchers point to &lsquobasal bias&rsquo as another way that scientists may get it wrong when choosing new model organisms.

This occurs when scientists choose an organism because it was the first to branch off from its ancestor, rather than because it has known genetic or developmental similarities to it.

&ldquoWe caution against this widely used rule of thumb, and advise the use of additional criteria, such as molecular branch lengths, to choose species as best representatives of ancestral body plans,&rdquo said Dr Jenner.

&ldquoJust because an organism has sprung from the base of the evolutionary tree does not make it more primitive and representative.

&ldquoEqually, those that became separate species further down the evolutionary line are not necessarily increasingly different from that common ancestor.

&ldquoAmong living species that descended from a particular common ancestor, those designated as &lsquobasal&rsquo are those that are separated from this ancestor by the smallest number of speciation events.

&ldquoSometimes evolution speeds up in association with speciation &ndash an organism can change a lot in this time.

&ldquoHowever, substantial evolutionary change may also occur in the absence of speciation, so basal species are not necessarily, or even likely a more conserved model of the ancestor.

&ldquoWe need to make better use of the techniques that allow us to calculate how much an organism&rsquos genome has changed over time, when making assessments about how much an animal resembles its ancestor, because this information can be helpful in estimating how much an organism&rsquos phenotype has changed.&rdquo

Dr Wills added: &ldquoEstablishing criteria for choosing model organisms is important in this field, especially given the pressure on available funding sources.

&ldquoWe encourage evo-devo workers to communicate with funding agents so that the limited resources available will not be disproportionately channelled to the &lsquobig six&rsquo, which, while important, cannot illuminate all evo-devo&rsquos central themes.

&ldquoIf we want to understand how insects evolved wings or how legs developed from fins, we need to judiciously choose several models from specific parts of the phylogenetic tree.

&ldquoThere is little point in blindly increasing the diversity of model systems, without some specific goals in mind.

&ldquoOur toolkit is too narrow, so as a community we need to clarify our objectives and set the agenda for future studies.&rdquo

Story Source:

Materials provided by University of Bath. Note: Content may be edited for style and length.


Watch the video: Evolutionary Developmental Biology (January 2022).