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Is there a formal definition of signature of natural selection?

Is there a formal definition of signature of natural selection?


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I've searched for a definition ofsignature of natural selection. Unfortunately, I haven't found any formal definition of it.

The signature of positive selection on standing genetic variation. I found this post that links to an article measuring the signature with an Fst and a Linkage Disequilibrium test.

Are we looking at only some frequency of alleles in different populations? Is the signature only a fixation of alleles in a population where this allele was positively selected?

Another article says:

A single beneficial substitution can be detected in polymorphism data, so long as it occurred recently. The fixation of a favorable allele in a population distorts patterns of variation at linked sites, thereby leaving a distinguishing signature that lasts up to about 104 generations in humans or about 106 in Drosophila melanogaster (Przeworski 2002). In principle, targets of positive selection can therefore be identified by searching polymorphism data for regions that harbor this signature (Nair et al. 2003; Wright et al. 2005).

Is it always in linkage disequilibrium?


Here is how I would personally explain what a genetic signature is:


A genetic signature of a certain type of evolutionary history (type of selection or demography) refers to the effect that this specific type of evolutionary history has on genomic properties of populations today.

We are interested in understanding such signatures because knowing the association between a number of evolutionary histories and their genetic signature allows us to reconstruct the evolutionary history of a population just by looking at the genomics of this population.

For example, recent selective sweep, population expansion after a recent bottleneck and/or linkage to a swept gene are all causing negative Tajima's D. Balancing selection and/or sudden population contraction are causing positive Tajima's D. In this example Tajima's D is a statistics that is used to measure this so-called signature of the different process.


The above was NOT a citation. I am NOT aware of any publications defining rigorously what a genetic signature is. I think it is not so uncommon for authors to use a term without strictly defining it. As long as the unclarity of the definition does not yield to limit cases and misunderstanding, then it is totally fine.


A genome-wide approach to identify genetic loci with a signature of natural selection in the Irish population

In this study we present a single population test (Ewens-Waterson) applied in a genomic context to investigate the presence of recent positive selection in the Irish population. The Irish population is an interesting focus for the investigation of recent selection since several lines of evidence suggest that it may have a relatively undisturbed genetic heritage.

Results

We first identified outlier single nucleotide polymorphisms (SNPs), from previously published genome-wide data, with high FST branch specification in a European-American population. Eight of these were chosen for further analysis. Evidence for selective history was assessed using the Ewens-Watterson's statistic calculated using Irish genotypes of microsatellites flanking the eight outlier SNPs. Evidence suggestive of selection was detected in three of these by comparison with a population-specific genome-wide empirical distribution of the Ewens-Watterson's statistic.

Conclusion

The cystic fibrosis gene, a disease that has a world maximum frequency in Ireland, was among the genes showing evidence of selection. In addition to the demonstrated utility in detecting a signature of natural selection, this approach has the particular advantage of speed. It also illustrates concordance between results drawn from alternative methods implemented in different populations.


Natural Selection Quotes

&ldquoWith savages, the weak in body or mind are soon eliminated and those that survive commonly exhibit a vigorous state of health. We civilised men, on the other hand, do our utmost to check the process of elimination we build asylums for the imbecile, the maimed, and the sick we institute poor-laws and our medical men exert their utmost skill to save the life of every one to the last moment. There is reason to believe that vaccination has preserved thousands, who from a weak constitution would formerly have succumbed to small-pox. Thus the weak members of civilised societies propagate their kind. No one who has attended to the breeding of domestic animals will doubt that this must be highly injurious to the race of man. It is surprising how soon a want of care, or care wrongly directed, leads to the degeneration of a domestic race but excepting in the case of man himself, hardly any one is so ignorant as to allow his worst animals to breed.

The aid which we feel impelled to give to the helpless is mainly an incidental result of the instinct of sympathy, which was originally acquired as part of the social instincts, but subsequently rendered, in the manner previously indicated, more tender and more widely diffused. Nor could we check our sympathy, if so urged by hard reason, without deterioration in the noblest part of our nature. The surgeon may harden himself whilst performing an operation, for he knows that he is acting for the good of his patient but if we were intentionally to neglect the weak and helpless, it could only be for a contingent benefit, with a certain and great present evil. Hence we must bear without complaining the undoubtedly bad effects of the weak surviving and propagating their kind but there appears to be at least one check in steady action, namely the weaker and inferior members of society not marrying so freely as the sound and this check might be indefinitely increased, though this is more to be hoped for than expected, by the weak in body or mind refraining from marriage.&rdquo
― Charles Darwin, The Descent of Man


Challenges to Inclusion of NOS in the Classroom

The inclusion of NOS in science teaching has not been without its issues, including discussions of NOS advocacy in science teaching, what version of NOS should be the focus of science instruction, distinctions in NOS between one science discipline and another, and NOS instructional models. These matters and more will be addressed in the next few pages.

Advocating for the Inclusion of NOS in Science Teaching

One of the most important impediments to the inclusion of NOS topics in science teaching has been the rather muted recommendations regarding NOS in science curriculum documents. In the United States, each state has responsibility for determining the curriculum in each school discipline. This has resulted in a proliferation of state-specific curriculum documents. A detailed study (McComas et al., 2012) of all of these documents revealed that most states offered some recommendations regarding NOS, but the policies were highly variable. Some states had a robust section on NOS instruction and included all of the specific elements often suggested by science educators, whereas other states barely mentioned the topic. In the reality of the classroom, it is unclear how frequently NOS was featured in instruction or on teacher-made or state-mandated end-of-course tests. Such inclusion would encourage teachers to provide some instruction in NOS.

The NOS situation improved slightly with the development of Project 2061 (1989), the related Benchmarks for Science Literacy (AAAS, 1993), and the National Science Education Standards (NSES National Research Council, 1996) which included some NOS recommendations. The NSES was an important document in that it was developed by a large group of experts and designed with the hope that states would adopt all or most of the recommendations. NSES generated much conversation but little concrete action, and states generally maintained their own science curriculum guidelines, although many were informed by aspects of the NSES.

The “standards movement” has recently reached a new level in science with the release of the Next Generation Science Standards (NGSS NGSS Lead States, 2013). The NGSS were generated by representatives from a group of “lead states,” with the goal that they be adopted wholly by those states and perhaps by many more. Since the 2013 release of the final NGSS document, there has been much debate, as each state is in a position to accept, reject, or modify the recommendations of the NGSS. It is clear that a majority of states will ultimately adopt the NGSS, making them a powerful tool for directing curriculum development, teacher preparation, and the design of shared assessments of educational progress. With widespread adoption of the recommendations provided in a single science education policy document, most students in the United States will be expected to learn the same science content. This is a historic moment in science education.

On first inspection, the NGSS offer three major recommendations to guide science teaching. These include (1) specific science content linked to grade levels (2) crosscutting concepts (e.g., patterns, stability and change, structure and function) that link the sciences and (3) shared science and engineering practices, such as asking questions, analyzing and interpreting data, arguing from evidence, and related ideas. In reality, the nature of science is a fourth major area advocated by NGSS that some might miss because of the way NOS is presented in the document. Unfortunately, the NOS recommendations do not have the prominence of the other three main NGSS elements, but finally there is a resounding voice of advocacy for NOS instruction. More about the specifics in a moment.

What Nature of Science Should We Teach?

The NOS issue that has generated the most conversation is the specifics about what to teach regarding this important topic. In a recent article, van Dijk (2012) suggested that “at present, a general characterization of the nature of science is still lacking and probably such a characterization will not be achievable” (p. 2142). From a philosopher’s perspective her statement is true at some level, but it is neither relevant to nor helpful in our discussions here. The position is not relevant because we are not trying to educate the next generation of philosophers of science, and not helpful because she offers no alternative and seems therefore to recommend that we continue teaching science without a reference to its nature.

In addition, one wonders what aspects of traditional science content would survive scrutiny if we evaluated all such content through van Dijk’s lens. For instance, consider the ways in which photosynthesis can be taught. Students can become acquainted with photosynthesis by learning the inputs and outputs (a shallow treatment, to be sure). Or students can fully explore the biochemical pathways, electron transport mechanisms, and fine detail of chloroplast structure and chlorophyll function. Most would argue that there is nothing wrong with the introductory view as long as it is essentially accurate. In this example, communicating the inputs and outputs of photosynthesis to students is certainly not incorrect, but it is not complete. However, such an introduction may afford students sufficient knowledge to be interested in learning more, perhaps through an AP Biology class or a semester-long botanical bioenergetics class at the university level, where the fascinating nature of photosynthesis can be fully investigated. The argument that the science education version of NOS is not complete or rich enough seems faulty unless those who offer it are prepared to attack all school science content as equally incomplete or shallow.

Finally, we can see that van Dijk’s argument is simply not helpful in that she proposes no alternative. She apparently fails to see that for most students, the school science experience is an introduction, not the final word on what we know about the natural world. Certainly, what we teach in science class must be accurate, but must it also be perfectly complete?

In recent decades, numerous recommendations have been offered for what NOS elements, aspects, or categories are most appropriate to enrich science instruction. Those by Lederman (2002, 2007) McComas (1998, 2004, 2008) and Osborne et al. (2003) align nicely with those in the NGSS. The approach of examining sets of recommendations for their common elements is called the “NOS consensus approach” and has been profitable in providing guidance regarding the element of NOS that might be integrated into science teaching.

The NOS recommendations in the NGSS are derived from this consensus perspective and are presented in an appendix and in two detailed tables, one linked to the practices of science (Categories I–IV) and another related to the crosscutting concepts (Categories V–VIII). The following is a list of the eight NOS categories in the NGSS.

I: Scientific Investigations Use a Variety of Methods

II: Scientific Knowledge Is Based on Empirical Evidence

III: Scientific Knowledge Is Open to Revision in Light of New Evidence

IV: Science Models, Laws, Mechanisms, and Theories Explain Natural Phenomena

V: Science Is a Way of Knowing

VI: Scientific Knowledge Assumes an Order and Consistency in Natural Systems

VII: Science Is a Human Endeavor

VIII: Science Addresses Questions about the Natural and Material World

Each of these categories is accompanied by illustrations that provide additional commentary, and many of these illustrations are further associated with the recommended science content, crosscutting concepts, and science and engineering practices.

There is much more good news than bad about NOS in the NGSS. However, some of the frequently recommended NOS elements, such as the role of creativity and subjectivity in science, are either missing entirely or hidden within the NOS illustrations, making a complete statement on what NOS to teach somewhat difficult for teachers to find. One of the NOS categories, “Science Is a Way of Knowing,” seems unnecessarily vague what does one teach to focus on this statement?

NOS for School Science Purposes: Beyond the Next Generation Standards

There is likely no list of NOS elements that all science educators would embrace, but some might see the conceptualization offered by McComas (2008) as a clear and comprehensive representation of NOS for school purposes. It is not possible to provide a full description of each of the recommended NOS elements here, but such a discussion can be found in McComas (2004, 2015). One can gain a reasonable overview by examining Figure 1 and the corresponding outline below, in which the nine elements are organized in three clusters.

The major elements of NOS appropriate for inclusion in science instruction, arranged in three related clusters (modified from McComas, 2008).

The major elements of NOS appropriate for inclusion in science instruction, arranged in three related clusters (modified from McComas, 2008).

Outline of Proposed Core NOS Ideas to Inform K–12 Science Curriculum Development, Instruction, & Science Teacher Education

Note: An asterisk indicates that the particular NOS idea is found in or implied by the NGSS (in appendix H and the associated illustrations). On this point, consider that the NOS principle that “science cannot answer all questions” is implied by the NGSS statement that “science addresses questions about the natural and material world.” This would seem to suggest that science does not address questions that do not pertain to the natural and/or material world thus, there are limits to science.

Tools and Products of Science

(1)* Science produces, demands, and relies on empirical evidence.

(2)* Knowledge production in science shares many common factors and shared habits of mind, norms, logical thinking, and methods, such as careful observation, careful data recording, and truthfulness in reporting. The shared aspects of scientific methodology include the following:

Experiments are a route, but not the only route, to knowledge.

Science uses both inductive reasoning and hypothetico-deductive testing.

Scientists make observations and produce inferences.

There is no single stepwise scientific method by which all science is done.

(3)* Laws and theories are related but distinct kinds of scientific knowledge.

Human Elements of Science

(4)* Science has a creative component.

(5) Observations, ideas, and conclusions in science are not entirely objective. This subjective (sometimes called ‘‘theory-laden”) aspect of science plays both positive and negative roles in scientific investigation.

(6)* Historical, cultural, and social factors influence the practice and direction of science. The topics of scientific inquiry are as much dictated – through funding and focus – by the needs of a particular society as they are by the curiosity of scientists.

Science Knowledge and Its Limits

(7) Science and engineering/technology influence each other but are not the same.

(8)* Scientific knowledge is tentative, durable, and self-correcting. (This means that science cannot prove anything, but scientific conclusions are valuable and long-lasting because of the way in which they are developed mistakes will be discovered and corrected as part of the process.)

(9)* Science and its methods cannot answer all questions. In other words, there are limits to the kinds of questions that may be asked within a scientific framework.

Much has been written – both pro and con – about “lists” such as those provided above, in the NGSS, and in various contributions to the literature. Those who support the contents of such lists are motivated not so much by a desire to present a full account of the history and/or philosophy of science, but more by the hope that students will learn something about how science functions by exploring what might best be called “NOS-for-school-purposes.” This is exactly the same goal as that of biochemistry-for-school-purposes, or photosynthesis, or taxonomy, or any other goal associated with the school science experience.

The Nature of Science across the Boundaries of the Sciences

Another criticism occasionally offered regarding NOS recommendations is that the nature of science does not function across the sciences in exactly the same fashion. For example, van Dijk (2011, p. 1086) states that the lists ignore “the actual heterogeneity of science” and questions “whether such consensus views can fruitfully contribute to the aim of science communication, i.e., to enhance the public’s functional scientific literacy.” There is some validity in this statement regarding the notion that the rules of the game of science operate somewhat differently in one science compared with another. However, are these differences so substantive as to derail attempts to look for commonality across the sciences? Philosopher of science Elliott Sober (2015) states that the answer is “No.” While he recognizes the various perspectives on this issue, he has concluded that “there are general normative principles that govern every science” (p. 195). Sober’s pragmatic perspective is shared by those who advocate the development of a generalized view of science designed specifically to inform the teaching of all sciences.

From the “big picture” perspective of how science works, there is far more in common between the sciences than not, and it is possible to provide some general normative practices worthy of discussion in all science classes. Even if we acknowledge some distinctions based on the context of a specific science discipline, from a pedagogical perspective it would make no sense to focus on the small differences in NOS if students fail to see the big transcendent ideas. We want students to know that scientific conclusions are based on evidence, that scientific ideas are potentially revised with new evidence, that science is a human endeavor, that science is limited to exploring aspects of the natural world, that creativity plays a role in all aspects of science, and that there is a distinction between the goals of science and those of engineering.


Proteoglycan vs Glycoprotein

A proteoglycan is a subtype of glycoprotein found in cell membranes within mucus and connective tissue it is sometimes called a mucoprotein. A mucoprotein is composed of core proteins covalently-bonded to glycosaminoglycans (GAGs).

A GAG is a chain of repeating disaccharide (simple sugar) molecules. The structure below depicts part of the cartilage matrix, an area where glycans are extremely prolific.

Any compound containing sugars (carbohydrates) attached to other chemicals is classed within the vast glycoconjugate group. Glycoconjugates can be further separated into glycoproteins, glycopeptides, peptidoglycans, glycolipids, and lipopolysaccharides. This article does not include carbohydrate lipid conjugates. As we have seen in proteoglycan vs glycoprotein, proteoglycans are a subcategory of glycoproteins.

Peptidoglycans or mureins are found only in bacteria. These conjugated carbohydrates are located in the cell wall and do not contain proteins but very short amino acid chains (oligopeptides). They help the bacteria to keep its shape and also aid passive transport (osmosis) through the bacterial cell wall.


Abstract

During their dispersal from Africa, our ancestors were exposed to new environments and diseases. Those who were better adapted to local conditions passed on their genes, including those conferring these benefits, with greater frequency. This process of natural selection left signatures in our genome that can be used to identify genes that might underlie variation in disease resistance or drug metabolism. These signatures are, however, confounded by population history and by variation in local recombination rates. Although this complexity makes finding adaptive polymorphisms a challenge, recent discoveries are instructing us how and where to look for the signatures of selection.


What is Biosemiotics?

The theory of zoosemiotics (Sebeok 1972) contributed to a further integration of biology and semiotics. Signs used by animals (visual, acoustic, and chemical) are processed by their nervous system in the same way as in humans. Thus, it was natural to expand semiotic notions from human semiotics to zoosemiotics (Sebeok 1972). Further studies indicated that interpretation of signs does not necessary require a nervous system. Krampen (1981) suggested that even plants are capable of interpreting signs although they have no nervous system. According to these studies, biosemiotics is a specialized branch of semiotics that focuses on communication in living systems. Pattee (1982) suggested that communication is the essential characteristic of life. Thus, biosemiotics should be viewed as a root of both biology and semiotics (Sharov 1992). Hoffmeyer (1997) developed this view showing that any organism is a message to future generations that describes the art of survival and reproduction.

The term "biosemiotic" was first used by F.S.Rothschild in 1962. For a detailed history of biosemiotics, see the paper of K. Kull.

Values and semantic closure

Usefulness is not a quality but a relation between an object and user. But at a closer look, a user is nothing but a collection of useful objects. Organs are tools that are used by an organism for performing specific functions, but there is nothing in the organism besides organs. Thus, the user is just a set of relations between useful parts. Obviously, not all kinds of relations can be considered useful. Some relations may destroy the system. Relations are useful only if they preserve and augment the same relations in the future, i.e., if these relations are self-reproducing. This idea was first formulated by Pattee (1982, 1995) and was called "semantic closure". Semantic closure is a new criterion for autonomy (or wholeness) of systems. A set of elements connected by relations is autonomous only if it is semantically closed, i.e., it reproduces itself in the future and defines its identity in the process of self-production. The value of each component or relation in an autonomous system corresponds to its contribution to the ability (or probability) of the system to reproduce itself.

The notion of value was introduced to biology by Fisher (1930). He defined reproductive value of an organism as its contribution to the growth of the entire population. For example, eggs have a smaller reproductive value than adults because adults can easily produce multiple eggs, but it takes a long time for an egg to develop into adult. In simple organisms that are not able to learn from their individual experience, natural selection is the mechanism that maximizes the value of organisms at each step in the life cycle. Higher animals can estimate the values before natural selection will take place. Thus, they are able to optimize their behavior without mortality. Fisher (1930) defined values only for entire organisms, but this definition can be extended to the parts of an organism and to relations between parts. The value of a part (or relation) is equal to its contribution to the process of self-reproduction. For example, the value of the resource is equal to the gain in reproductive value of an organism that captured this resource unit.

Biologists may ask why to use semiotic terminology in simple population models? In particular, why to talk about semantic closure instead of self-reproduction? "Self-reproduction" seems to be a convenient term that does not have uncertainties associated with signs or semantics. But this simplicity is illusive self-reproduction includes the word "self" which comes from the field of semiotics rather than physics or biology. In the process of self-reproduction, an organism defines itself in other words, self is what is preserved in the process of self-reproduction. Self-reproduction is simultaneously a process of self-measurement, self-interpretation, and communication from parents to offsprings.

Peirce (1955) defined a sign as a triadic relationship between a sign vehicle (representamen), an object, and interpretant which is a representation of the object in human mind invoked by the sign vehicle. The interpretant is a mental model of an object. Bacteria are not able to build mental models of objects but they can build material models of themselves, i.e. their offspring. Genome can be viewed as a sign vehicle that is interpreted in offsprings. It tells offspring organisms how to develop, survive, and reproduce. The message is true because it was verified by natural selection in numerous generations. In simple autocatalytic systems, genome is not represented by a specialized structure (e.g., DNA), and the entire system can be viewed as a message (i.e., sign).

Normal communication requires that signs have positive values both for a producer and receiver. An organism spends its resources to produce a sign only if the sign has value, i.e., it increases the rate of self-reproduction either directly or indirectly. In the same way, the receiver never interprets a message unless it expects to increase its fitness after interpretation. Here I mean expectation in a broad sense including evolutionary (unconscious) expectation. Only in higher animals and humans expectation becomes conscious. But in some cases, the value of signs may be negative. For example, some predators may intercept signals produced by their prey. In this case, the value of a sign is negative for the producer. Other predators may emulate signs that attract their prey. In this case, the value of a sign is negative for the receiver. But negative values are not normal. If a sign has negative value too often, then organisms will simply avoid using it.

Human signs also have values, but this value is no longer connected with biological reproduction. Human evolution is driven more by the propagation of life styles (memes) rather than by propagation of genes. Memes are associated with specific human relations (e.g., ethical, religious, educational, etc.). The value of texts is associated with propagation of these relations. Peirce (1955) described only a half of the life cycle of a sign, i.e., the process of perception and recognition. He did not analyze the process of sign production which closes the cycle (semantic closure). According to Pattee (1995) each sign participates in a larger system with semantic closure.

Metasystem Transition

Symbiosis is another mechanism of metasystem transitions which was not mentioned by Turchin (1977). Several non-similar systems may start cooperating, and this cooperation represents semantic closure at a higher level. For example, lichens are symbiotic organisms that originated from fungi and algae eucaryotic cells originated from a symbiosis of several types of procaryotic cells.

When two autocatalytic systems cooperate, they produce resources for each other. Resources, as we have seen in the previous section, are primitive signs. Thus, cooperation is a semiotic relationship. Each component has a double interpretation in such a system. First, it is self-reproducing on its own (self-interpretation), and second, it produces signs that are interpreted by another component.

The major obstacle on the way of cooperation is possible evolutionary instability. Let us consider cooperating chemical species A and B that produce resources for each other. Species A may "mutate" into a selfish species A1 which will use resources produced by species B without providing help to the species B. As a result, the cooperation between species becomes broken. Cooperation is evolutionary stable only if specific restrictions are applied on communication (resource exchange). I call these restrictions "encapsulation". For example, several representatives of species A and B may form small groups, so that communication occurs only among members of a group. If a selfish mutation destroys communication within a group, then this group will become less competitive and eventually will be eliminated in the process of group selection. Thus, encapsulation makes a metasystem transition possible. Hierarchical systems have several levels of encapsulation and this makes them more stable than systems that have no communication restrictions.

Each self-reproducing component in a hierarchical system defines its own values. Thus, there is a hierarchy of values. A message may have value for several components. For example, growth hormones stimulate proliferation of individual cells and thus, they have a positive value for a cell. But the same hormones may change the morphology and function of a multicellular organism. These changes may be beneficial or harmful. Also, growth hormones may cause cancer which is fatal for an organism.

The value of an element at a higher level may be reduced if there is a conflict of values among its components. Thus, natural selection favors those hierarchies in which conflicts are minimized. The fitness landscape of components usually does not have a single sharp peak. Instead, there is a region of neutrality where fitness is almost the same. Thus, the super-system may examine neutrality regions of its sub-systems and find points where conflicts between sub-systems are minimized. The system creates a higher-level value without damaging lower-level values.

Biosemiotics and philosophy

My views are most closely related to pragmatism of Peirce and James. According to pragmatism, the ultimate measure of truth is usefulness (=value).Pragmatism is opposed to objectivism (realism) which assumes that our knowledge of the real world is objective and absolute. Realism includes materialism and objective idealism. Because knowledge is expressed in language, realists assume that words have observer-independent meanings. Their favorite model of language is formal logic in which truth-values are specified in a meta-language. Truth is determined syntactically in the same way as we prove theorems. The correspondence between facts and theories is syntactic too because both are expressed in the same formal language. Tarski considered the sentence "It is raining" true if it is really raining. But there is no exhaustive definition of "raining" any existing definition can be further clarified and specified. Even the word "water" can not be defined (see the discussion on twin Earth).

Biosemiotics, on the contrary, emphasizes the role of observer/agency. Each living organism builds its own world, its own reality (Umwelt). This world includes the body and surrounding objects which an organism uses in its activities. Advanced organisms can produce signs that correspond to elements of their Umwelt. Each human being has its thesaurus by which he can describe his Umwelt. A language is a result of agreement among communicating individuals on how to use specific signs. Thus, sentences in human language have no absolute and fixed meaning meaning is conventional. Testing hypotheses is a semiotic process: we apply old words to new objects (widening or narrowing the meaning of words). Instead of a uniform objective knowledge for everybody I prefer to consider different kinds (i.e., levels) of knowledge. Each person gets the knowledge he is capable to grasp. The whole process of evolution is the movement from primitive knowledge to more and more advanced knowledge

Biosemiotics is close to evolutionary epistemology in the following aspects. It considers knowledge as a natural phenomenon which can be studied using the evolutionary theory. Biosemiotics is a descriptive science and rejects normative epistemology and normative ethics. In other words, it does not tell you how to learn and how to live. Instead, it describes how learning usually occurs and what strategies of life are usually successful. This does not mean that normative epistemology and ethics should be dismissed. It is known that some people want to know explanations whereas other people are satisfied with instructions and directions. Normative epistemology and ethics are definitely needed for people in the second category.

However, most people who develop evolutionary epistemology are realists, including the founders (Campbell, Lorenz, and Popper). Popper thought that scientific language has fixed meaning, and therefore scientific knowledge is objective and absolute. I think that there is no absolute (observer-independent) knowledge. However, there may be levels of "objectivity" depending on the scope of usefulness of knowledge (how large is the group of people who apply this knowledge and how successful this application was in the past). In this sense, scientific knowledge is more "objective" than superstitions.


Is there a formal definition of signature of natural selection? - Biology

About a year ago, I wrote a blog post about the pervasive misrepresentation of natural selection, not only in the mass media but also by professionals who should know better. My main problem is with the depiction of natural selection as an intentional process, as in “The cockroaches evolved a clever solution in order to avoid the pesticide” or “The cockroaches had to evolve in order to survive.”

Last week, my husband and colleague Doug Gaffin provided evidence from his class that this misconception is common, but that good teaching can start to turn it around. Before he began his unit on evolution, he asked the hundreds of students in his introductory zoology class to respond to several true/false clicker questions:

  1. Evolution results in progress organisms are always getting better through evolution.
  2. Humans are the pinnacle of evolution and have stopped evolving.
  3. Evolution is not science because no one has observed it happen.
  4. Organisms adapt when needed so they can increase their survival.
  5. Natural selection gives organisms the traits they need.
  6. If humans evolved from apes, then all apes should have evolved into humans.

He asked the questions again after the evolution unit was complete. Most students (82-92% of them) already knew that #2, #3, and #6 were false before the unit even started their responses were virtually unchanged in the “before” and “after” sessions.

Question #1 also showed virtually no change about half of the students agreed with the statement before and after instruction in evolution. However, I find the question confusing. It is false because organisms don’t necessarily get “better,” whatever that might mean, and mutations will continue to occur. But it is also true because natural selection tends to weed out the harmful mutations. So that one doesn’t seem very informative to me.

The most interesting responses were to questions #4 and #5. Before instruction in evolution, a whopping 92% of students incorrectly agreed that “organisms adapt when needed so they can increase their survival.” Afterwards, that number dropped to 41%. The improvements were less dramatic for question #5. About 66% of students incorrectly thought that “natural selection gives organisms the traits they need” before the evolution unit started. Only 32% of students agreed with that statement after the unit was complete.


Both #4 and #5 get at the heart of the “organisms can evolve on purpose” misconception. On the one hand, it’s nice to see that good teaching can make a difference. On the other hand, it’s frustrating that more than a third of the students held onto that misconception even after weeks of instruction.

I will continue to look for resources and activities to help students overcome this difficulty. If you have any to share, please add them to the comments section. I thank you, as will your teaching colleagues.


INTRODUCTION

Phylogeographic studies over the past decade have uncovered how past climate oscillations have repeatedly impacted on species ranges and influenced the genetic composition of taxa (Hewitt, 1996 Hampe and Petit, 2005 Schönswetter et al., 2005). Across a distribution range, climate changes trigger the regression of populations where conditions become suboptimal and they render new territories suitable for colonization (Ackerly, 2003). Accordingly, two types of margins can be distinguished: the trailing edge, where populations persist despite climate changes, and the leading edge, where populations expand following climate changes. The Alps went through drastic climatic oscillations during the Pleistocene and represent an excellent landscape study system to explore the consequences of climate-induced range-shifts. There is now accumulating evidence demonstrating long-term persistence of several plant taxa, often at the border of the Alps, and postglacial recolonization from the peripheral to the central Alps and towards higher elevation (Schönswetter et al., 2005 Parisod, 2008). Consequently, the Alps harbour populations with a varying, contrasting history over a restricted geographical scale (i.e. similar climatic conditions, landscapes, biogeographical areas), which may greatly help to understand the evolutionary mechanisms underlying patterns of genetic variation under changing climate.

The focus here is on the widespread alpine herb Biscutella laevigata (Brassicaceae). At the regional scale, the influence of past climate changes on the species range was evidenced as a gradient of genetic diversity along recolonization pathways (Parisod and Besnard, 2007). Accordingly, the peripheral Alps represent the historical core of lineages as attested by ample haplotypic variation among localities and presently correspond to trailing-edge conditions. In contrast, recently glaciated areas at high altitude in the central Alps are typically formed of expanding populations fixed for a single haplotype and correspond to the leading edge. Taking advantage of such historical demographic perspective offered by phylogeography, selected marginal populations of B. laevigata within a particular haplotype lineage (E according to Parisod and Besnard, 2007) were examined at a local scale with amplified fragment length polymorphism (AFLP): one population corresponding to the trailing edge in the peripheral Alps (Parisod and Christin, 2008) and another population corresponding to the leading edge in the central Alps (Parisod and Bonvin, 2008).

Both populations showed similar total genetic diversity and presented mosaic spatial patterns of genetic differentiation, but distinct patterns of within-population genetic structure. The trailing-edge population presented a significantly lower within-plot genetic diversity than the leading edge (Shannon index: 0򷂓 ± 0򷀳 vs. 0򷉰 ± 0򷁗, respectively Wilcoxon test, P < 0򷀁) and a stronger internal genetic structure (between-group eigenanalysis: βST = 0띉 vs. βST = 0뜲, respectively). Furthermore, individuals with similar AFLP profiles were consistently associated with ecological factors in the trailing-edge population, suggesting local adaptation with respect to total solar radiation (Parisod and Christin, 2008). In contrast, the leading-edge population manifested signatures of recent expansion since the distribution of both individual abundance and genetic diversity formed a cline along the local recolonization path (Parisod and Bonvin, 2008). Moreover, sampling plots from in between genetically homogeneous patches consistently showed higher genetic diversity, indicating ongoing admixture after recolonization.

The aim of this study is to explore the impact of divergent natural selection in different types of range margins by comparing indirect estimates of gene flow and genome scans for signature of selection between these trailing-edge and leading-edge populations. According to the hypothesis of Jansson and Dynesius (2002) postulating selection for local adaptation in areas with little climate-enforced range shifts, a clear signature of selection in the trailing-edge population and none or only a low one in the leading-edge population is expected.


Reviewing “Darwin’s Doubt”: Response by Stephen Meyer

I appreciate the close reading and careful evaluation of my book, Darwin’s Doubt ( DD ) by the authors of the multi-part review series published recently on the BioLogos website. I would like to thank the main reviewers of the book (Ralph Stearley, Robert Bishop, and Darrel Falk) for taking the time to read and review the book as well as BioLogos and its new President Deborah Haarsma for their decision to highlight these reviews and their generous invitation to me to submit this response. Anyone whose work receives such scrutiny, with such a breadth of coverage, will learn something, and I certainly have.

I have especially appreciated how the reviews in this recent series have unexpectedly clarified the nature of disagreement between proponents of the theory of intelligent design (ID) and the proponents of theistic evolution (or evolutionary creation) associated with BioLogos. I—and many others—have long assumed that the debate between our two groups was mainly a scientific debate about the adequacy of contemporary evolutionary theory. Surprisingly, the reviews collectively have shown that the main disagreement between ID proponents and BioLogos is not scientific, but rather philosophical and methodological.

In particular, the reviews have revealed that the central issue dividing the BioLogos writers from intelligent design (ID) theorists concerns a principle known as methodological naturalism (MN). MN asserts that scientists must explain all events and phenomena by reference to strictly naturalistic or materialistic causes. The principle forbids postulating the actions of personal agency, mind, or intelligent causation in scientific explanations and thus limits the explanatory toolkit of science to strictly material processes or physical causes. The principle of methodological naturalism is, of course, not a scientific theory nor an empirical finding, but an allegedly normative methodological rule, against which I have argued in depth, both in Darwin’s Doubt (see Chapter 19) and in my earlier book, Signature in the Cell (see Chapters 18 and 19). My colleagues have also argued against MN in their responses to some of the BioLogos reviews of Darwin’s Doubt (see, for example, here and here).

Recall that Darwin’s Doubt argues that intelligent design provides the best explanation for the origin of the genetic (and epigenetic) information necessary to produce the novel forms of animal life that arose in the Cambrian period. In making this case, I show first that neither the neo-Darwinian mechanism of natural selection acting on random mutations, nor more recently-proposed mechanisms of evolutionary change (species selection, self-organization, neutral evolution, natural genetic evolution, etc.—see Darwin’s Doubt Chapters 15-16) are sufficient to generate the biological information that arises in the Cambrian period. Instead, I show—based upon our uniform and repeated experience—that only intelligent agents have demonstrated the power to generate the kind of functional information that is present in biological systems (and that arises with the Cambrian animals). Thus, I conclude that the action of a designing intelligence provides the best (“most causally adequate”) explanation for the origin of that information.

Now, one might have expected that Ralph Stearley, a paleontologist, and Darrel Falk, a geneticist, both of whom have extensive knowledge of evolutionary theory, would have critiqued the main scientific argument of Darwin’s Doubt on scientific grounds. In particular, one might have expected that they would have argued that either the neo-Darwinian mechanism, or some other evolutionary mechanism, does have the creative power to produce the information necessary to build new forms of animal life. Instead, except for raising a few minor objections about incidental scientific matters, both acknowledged that evolutionary theory has left the problem of the Cambrian explosion unsolved—i.e., that the mutation/natural selection mechanism lacks the creative power to account for macro-evolutionary innovations in the history of life.

Falk, for instance, wrote that Darwin’s Doubt identifies “one of the great mysteries in evolutionary biology today,” namely, the origin of animal form. Falk observed that this problem has never really been addressed by neo-Darwinian theory, and reflected on his own experiences as a college teacher of evolution discovering the shortcomings of textbook theory when confronted with the origin of complex animal evolution. He added that the process of natural selection, important as it may be in certain contexts, is not the “driving mechanism” of macro-evolutionary change, and thus, that the mystery of the Cambrian explosion still awaits a solution.

Of course, Falk himself rejects my proposed solution and my positive argument for intelligent design as the best explanation for what I call the “Cambrian information explosion.” He contends that any such inference to intelligent design is “premature.” Nevertheless, Falk doesn’t really offer any evidence or scientific reason for rejecting the positive argument of Darwin’s Doubt . Indeed, it would be difficult for him to deny that intelligent agents possess the causal power to produce functional information. Is it possible, then, that his reluctance to consider intelligent design as the best or “most causally adequate” explanation stems from a tacit commitment to methodological naturalism? If inferences to intelligent design are perceived as breaking the rules of science then, of course, they will always be seen as “premature.”

Stearley also found value in the book’s scientific analysis, saying that the book “makes an argument that folks should think hard about” and indeed that he “resonate[s] with some of Meyer’s arguments.” Although he was unhappy with aspects of DD ’s treatment (he thinks I should have talked more about the small shelly fossils in the early Cambrian, for example—something my colleagues and I have already addressed in responses to critics here and here), Stearley agreed with my critique of the adequacy of current evolutionary mechanisms for the origin of animal form. Thus, Stearly notes that I had “developed a case for the inadequacy” of standard approaches. On scientific grounds, therefore, relatively little of note separates us. In fact, Stearley admitted that he was “inclined to see design in nature,” but he too demurred from affirming the design hypothesis, offering hesitant uncertainty in response to my positive case. Could it be that in Stearley’s reluctance, we may, again, be seeing a tacit commitment to methodological naturalism?

Of the three reviewers, Wheaton College philosopher of science Robert Bishop was the least persuaded by DD ’s arguments—but, interestingly, he was also the most explicitly committed to the principle of methodological naturalism. Indeed, he objected to the thesis of the book precisely because it openly rejects (and violates) the principle of methodological naturalism.

Consequently, his four-part critique, by far the longest in the BioLogos series, said very little about my scientific arguments. (He did argue that I was wrong to claim that newer models of evolutionary theory represent significant deviations from neo-Darwinian orthodoxy. Yet, notably, biologist Darrel Falk’s review affirmed my assessment of these newer theories over and against Bishop’s.) In any case, Bishop focused his critique on what he called my “rhetorical strategies,” giving particular attention to philosophical issues concerning the legitimacy of design inferences in biology.

In Bishop’s judgment, intelligent design flagrantly violates the rule of methodological naturalism—a rule that he regards as normative for the practice of all natural science because he believes (incorrectly, as it turns out) that “methodological naturalism is the way scientific investigation has been done since before the time of the Scientific Revolution.” Indeed, as my colleague Paul Nelson pointed out in his response to Bishop’s critique, Bishop badly misreads the history of science. The design arguments developed by Isaac Newton—in the Opticks and the Principia , for instance —alone contradict Bishop’s claims.

Even so, Bishop correctly notes that methodological naturalism does categorically exclude consideration of inferences to the activity of non-physical entities or causes (i.e., intelligent agents or minds) in evolutionary or historical biology. These fields simply do not allow reference to the activity of intelligent agents. Bishop appears to justify this prohibition by claiming that “an intelligent agent is a presupposition external to cellular and evolutionary biology intelligence has to be brought in from the outside”—a move that, in his view, would transgress the boundaries of natural science and that “biologists rightly object to.”

Of course, asserting that methodological naturalism prohibits design inferences and then justifying that prohibition by arguing that inferring intelligent design would transgress the boundaries of science as determined by methodological naturalism is to argue in a circle. Further, as Paul Nelson pointed out:

Bishop completely misunderstands the basis of Meyer’s case for intelligent design. True, the intelligent agency that Meyer invokes to explain the origin of the information present in animal forms is “external” to the present operation of cells in those animals, just as the intelligence responsible for the design of a laptop computer is external to it. But that does not mean that Meyer “presupposes” that an agent “external to cellular and evolutionary biology” caused the origin of the information that arose in the Cambrian explosion of animal life. Instead, Meyer infers that a designing intelligence external to the features of cells and animals generated that information and he does so based upon our knowledge of cause and effect and [the] information-rich structures present in living systems. Since, as he argues, intelligence or mental activity is the only known cause of the origin of large amounts of functional or specified information, especially when that information is found in a digital form, the origin of the enormous amount of specified information that arose in the Cambrian period is best explained by the activity of a designing intelligence. Intelligence is not presupposed it is inferred based upon what we know about the cause, indeed the only known cause, of specified information.

In any case, by focusing his critique on the allegedly normative status of methodological naturalism, and DD ’s repudiation of that methodological convention as a normative rule for science, Bishop did not focus his critique on the scientific claims or analysis of the book.

Thus, both Bishop’s review (which challenged the methodological approach, but not the scientific analysis, of the book), and Falk and Stearley’s reviews (both of which conceded my main scientific critique of evolutionary theory) have helped to clarify the true nature of our disagreement. Since I look forward to further dialogue with our colleagues at BioLogos, I regard these reviews as a constructive first step to further discussion of the key issues that separate us.

As we continue to our discussion, I hope we can address the central issue about which we disagree. As noted, I have developed a detailed critique of methodological naturalism in my published work. I have shown, for example, that the demarcation criteria typically offered as justifications for methodological naturalism invariably fail to distinguish the scientific status of intelligent design and competing evolutionary theories. I have also argued that the principle of methodological naturalism restricts the intellectual freedom of scientists and compels them to elect materialistic explanations, whatever the evidence may indicate. As such, I argue that the principle impedes the truth-seeking (as opposed to convention-following) function of science.

Given my own skepticism about methodological naturalism, I would very much like to know what Darrel Falk and Ralph Stearley think about the principle and its alleged status as rule governing scientific reasoning. Their reviews express hints that design inferences in historical biology might be acceptable to them—yet those same reviews reveal a deep ambivalence about challenging the naturalistic premises of current evolutionary theory, or more fundamentally, about challenging MN itself.

Unfortunately, methodological naturalism is a demanding doctrine. The rule does not say “try finding a materialistic cause but keep intelligent design in the mix of live possibilities, in light of what the evidence might show.” Rather, MN tells you that you simply must posit a material or physical cause, whatever the evidence. One cannot discover evidence of the activity of a designing mind or intelligence at work in the history of life because the design hypothesis has been excluded from consideration, before considering the evidence, by the doctrine of methodological naturalism (and the definition of science that follows from it).

Nevertheless, having a philosophical rule dictate that one may not infer or posit certain types of causes, whatever the evidence, seems an exceedingly odd way for science to proceed. Scientists tend to be realists about the power of evidence, but skeptics about philosophical barriers—which, if it is anything, the rule of MN surely is. Placing the detection of intelligent design out of the reach of scientific investigation, before the evidence has had a chance to instruct us, looks like rigging a game before any players have taken the field.

In the debate about intelligent design, MN has compelled many scientists to dismiss evidence for intelligent agency as an explanation for phenomena such as increases in large functional digital information, that are known to be produced by one—and only one—kind of cause, namely, intelligent activity. Proponents of intelligent design reject this restriction precisely because it compromises the truth-seeking function of science. We insist that scientists should seek the best explanation, based upon our knowledge of the evidence and the causal powers of competing explanatory entities, not seek the best explanation only among an artificially restricted set of options. Our BioLogos colleagues appear to disagree.

This is the remaining issue, and it won’t be easy to resolve, because unlike scientific disputes where new evidence can break an impasse, MN keeps the evidence itself out of the discussion. Yet, in our view, if ever a rule of method deserved to be tossed onto the rubbish heap of history, now is the time for MN to be sent in that direction. Natural science has nothing to fear from allowing scientists to consider evidence for design hypotheses because (given the general cultural climate) their rigorous testing is assured, as the vigorous attacks on notions such as “irreducible complexity” and “specified complexity” over the past two decades have already shown. Many scientists have attempted to burnish their scientific standing by publishing challenges to claims made by proponents of intelligent design.

In a similar vein, as we continue our discussion, I would invite our colleagues at BioLogos to engage and reply to our critique of the principle of methodological naturalism—to defend, rather than just assert (as even Bishop mainly did), the normative status of MN. Offering such a defense will doubtless afford further opportunities for clarification and discussion of the key issues.

Constructive dialogue between parties with significant disagreements can, in the best case, expose both common ground and the true nature of those disagreements. The reviews recently published on the BioLogos website, have done both—a fact for which I, as the author of the book under discussion, am genuinely grateful.


Watch the video: Electronic signatures explained Part 4: How to know electronic signature is genuine? Signicat (June 2022).


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