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What is the Specific Nature of Observable Micro-evolution

What is the Specific Nature of Observable Micro-evolution


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I am wondering what the proven mechanisms of micro-evolution are. For instance, in the case of bacteria, when a strain evolves that is resistant to antibiotics

1) Is the genetic combination that enables the bacteria to resist the antibiotics present the entire time and only made prevalent by the environment through natural selection, or do mutations occur to the DNA that change the organism so that it can then survive?

2) With the antibiotics removed, is the evolved strain generally more resilient in a normal environment or weaker?


There are several mechanisms through which a bacteria can evolve antibiotic resistance.

One way is by acquiring an already-existing gene from another bacteria or virus by what is called horizontal gene transfer. In this case, there is no requirement for any mutation to occur although the new gene can be integrated into the host genome and so, in a sense, the genome itself is mutated since it now contains a new gene but the gene itself is not.

Another way of acquiring resistance is indeed by genetic mutations. One beautiful example of this is the resistance acquired toward some aminoglycoside antibiotics. This class of antibiotics interferes with the ribosome assembly by binding to a specific site of it. In some cases, a simple point mutation of the aminoglycoside-binding-site is enough to acquire resistance. Some strains of bacteria exhibit aminoglycoside resistance due to a transport defect (mutation of a channel for example) or membrane impermeabilization (mutation of a pump), acquired by other mutations. Others use specific enzymes to digest the antibiotic, different variants (mutants) of such enzymes broaden the spectrum of resistance. There are many other different cases in which a simple mutation is enough to acquire resistance.

Here some references:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC182597/

http://www.uphs.upenn.edu/bugdrug/antibiotic_manual/aminoglycosideresistance.htm

http://www.antibioresistance.be/aminoglycosides.html

https://www.ncbi.nlm.nih.gov/pubmed/27690121

https://www.ncbi.nlm.nih.gov/pubmed/27651364

https://www.ncbi.nlm.nih.gov/pubmed/27699821

With the antibiotics removed, is the evolved strain generally more resilient in a normal environment or weaker? No, in general, there is no extra advantage in being resistant to an antibiotic if the antibiotic is not present.


How warp-speed evolution is transforming ecology

Rachael Lallensack is a journalist based in Washington DC.

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The coloration of stick insects such as this Timema bartmani help it to hide, but might also affect the local ecology. Credit: Moritz Muschick

It took Timothy Farkas less than a week to catch and relocate 1,500 stick insects in the Santa Ynez mountains in southern California. His main tool was an actual stick.

“It feels kind of brutish,” says Farkas. “You just pick a stick up off the ground and beat the crap out of a bush.” That low-tech approach dislodged hordes of stick insects that the team easily plucked off the dirt.

On this hillside outside Santa Barbara, there are two kinds of bush that the stick insect (Timema cristinae) inhabits. The creature comes in two corresponding colorations: green and striped. Farkas and his fellow ecologists knew that the stick insects had evolved to blend in with their surroundings. But the researchers wanted to see whether they could turn this relationship around, so that an evolved trait — camouflage — would affect the organism’s ecology.

To find out, the team relocated mixtures of green and striped insects to different plants, so that some insects’ coloration clashed with their new home. Suddenly maladapted, these insects became targets for hungry birds, and that caused a domino effect 1 . Birds drawn to bushes with mismatched stick insects stuck around to eat other residents, such as caterpillars and beetles, stripping some plants clean. “That this evolutionary force can cause local extinction is striking,” says Farkas, an ecologist at the University of New Mexico in Albuquerque. “It affects the entire community.” All this happened because of an out-of-place evolutionary trait.

Ecologists have generally ignored evolution when studying their systems they thought it was impossible to test whether such a slow process could change ecosystems on observable timescales. But they have come to realize that evolution can happen more quickly than they assumed, and a wave of studies has capitalized on this idea to observe evolution and ecology in unison.

Such eco-evolutionary dynamics could be important for understanding how new populations emerge, or for predicting when one might go extinct. Experiments suggest that evolutionary changes alter some ecosystems just as much as shifts in more-conventional ecological elements, such as the amount of light reaching a habitat. “Eco-evolutionary dynamics is the dragon lots of people are chasing right now,” says Troy Simon, an ecologist at the University of Georgia in Athens.

Rapid evolution can sometimes offset some of the detrimental effects of a warming climate and other known drivers of change in other cases, it can worsen those effects. Even for the most common processes, such as changes in population size or food chains, ecologists must take evolution into consideration, researchers say. “Everybody realized rapid evolution was occurring everywhere,” says evolutionary ecologist Andrew Hendry of McGill University in Montreal, Canada.

It all goes back to Charles Darwin’s finches. When the naturalist visited Ecuador’s Galapagos Islands in 1835, he documented some variation in the beaks of finches living on different islands and eating different foods. Years after the voyage, he hinted in his Journal of Researches that this variation suggested a tight relationship between the birds’ ecology and their evolution.

Darwin never imagined seeing this in action, because he thought that evolution occurs only at the “long lapse of ages”. But by the late 1990s, ecologists had started to realize that evolution could be observed within a few generations of a given species — a timescale that they could work with.

Organisms that live and die quickly provided some of the early data demonstrating how evolution influences ecology. A key study 2 published in 2003 focused on algae and rotifers, microscopic predators that feed on algae both species can tick through up to 20 generations in the course of a couple of weeks. The study mixed the organisms together in tanks and showed that when algae evolve rapidly, they throw off normal predator–prey population dynamics.

Usually, the two species play out a cycle between ‘boom’ and ‘bust’. The algal population grows the rotifers then gobble them up and their own population explodes. When the predators have depleted the algae, their numbers crash. The algae then rebound and the pattern starts again. But when the researchers introduced different algal varieties — seeding some genetic diversity — the algae began to evolve rapidly and the cycle changed completely. The algal population remained elevated for longer, and the rotifers’ own boom was abnormally delayed because the new algae were more resistant to predation.

Similar studies in aphids 3 and water fleas 4 have confirmed that rapid evolution can affect characteristics of populations, such as how fast they grow. These ecological changes can alter future rounds of evolution and selection. Seeing such rapid evolution in action has changed ecologists’ picture of what they thought was a predictable and fundamental ecological process, and showed how important it is to consider evolution when studying how populations interact. “Everything about ecology has to be re-examined in light of the fact that evolution is more important than we thought,” says Stephen Ellner, an ecologist at Cornell University in Ithaca, New York. “This changes everything.”

After these initial lab studies, ecologists started to think bigger. Experiments conducted indoors at small scales can’t reproduce the intricacies of natural ecosystems, so researchers have been testing their ideas in grander, less artificial set-ups.

Working out whether eco-evolutionary dynamics affect the real world is one of the field’s biggest challenges, says Rebecca Best, an evolutionary ecologist at Northern Arizona University in Flagstaff, because so many uncontrollable factors can affect wild ecosystems.

She has found a middle ground by incorporating natural elements into a tightly controlled experiment. At a site overlooking Lake Lucerne in Switzerland, she and her team set up 50 miniature lakes: large plastic tanks each holding 1,000 litres of water, plus a slurry of sediment, plant life, algae, invertebrates and water collected from three lakes — Geneva, Constance and Lucerne. Once these ‘mesocosms’ were settled, with plankton reproducing and plants taking root, the team introduced into each tank one of two genetically distinct lineages of adult threespine sticklebacks (Gasterosteus aculeatus): one lineage from Lake Constance and the other from Lake Geneva. A few weeks later, the researchers removed the fish and replaced them with a mixture of lab-raised juveniles from both locations, plus some hybrids of the two lineages.

They found 5 that how the adults had manipulated their environments affected the survival of the next generation of fish (see ‘Fishy feedback’). If the adult fish removed prey of a certain size, for example, younger fish that shared characteristics with the adults — in this case, mouth size — went hungry. Juveniles that were different from the former occupants fared better. The study showed that the traits of the adult fish shaped the environment for the next generation — enough to dictate the evolutionary trajectory of those that followed.

Best says that her mesocosm experiments are more sophisticated and realistic than lab studies, but less easy to control. Ideally, she says, the team would run the experiment in the field, but that would come with its own obstacles, such as having to factor in the evolution of other species in the ecosystem, or the risk of events such as extreme storms.

Experiments such as Best’s are “vastly easier and more controlled than anything you can do in nature”, Hendry says. But they might not reflect what happens in real ecosystems. “That’s the watershed moment we’re at right now. Does this actually play out in the real world?”

In the messy real world, it can be difficult to pinpoint the impact of a single feature, either an ecological attribute (such as rainfall) or an evolutionary one (such as a change in camouflage).

A few intrepid ecologists are trying anyway. Last year, a study 6 on guppies in Trinidad demonstrated that the fish’s evolution can drive an ecological change as strongly as an environmental factor: the amount of light available.

The study focused on two populations of guppies (Poecilia reticulata) in the northern part of the island. Their habitats differ in several ecological characteristics, including how much shade they receive from the forest canopy, which affects how many algae grow in the streams.

The team moved populations of guppies — which differed in evolved traits such as body proportions and colour — between eight rivers in the watershed, and measured the canopy above the water. In some of the study sites, introducing a new kind of guppy altered algal populations as much as allowing 20% more light to stream onto the water did. Even a natural ecosystem, say the researchers, is a product of evolution as well as ecology.

This experiment did use a more natural setting than many others, but Trinidadian guppies are ecological celebrities that have appeared in hundreds of studies, and the rivers they inhabit have been highly manipulated already. Researchers want to know whether the forces at work in the guppy populations also play out in species that are not necessarily famous for evolutionary dynamics, says McGill ecologist Gregor Fussmann. “We need systems that are generic,” he says.

That’s exactly what Thomas Schoener, an evolutionary ecologist at the University of California, Davis, and his team have set out to do with two populations of lizard in the Bahamas. Their project is part of an ongoing multigenerational study, begun in 1977. They have been attempting to simulate accelerated evolution by catching curly-tailed lizards (Leiocephalus carinatus) and moving them to a string of tiny islands inhabited by brown anoles (Anolis sagrei), to see how the ecosystems change as a result.

Curly-tails are natural predators of the smaller brown anole, so when the team first moved the curly-tails onto islands with the anoles, populations of the latter dropped 7 . Spider populations increased when anoles — their main predator — took a hit, and the excess spiders then ate more springtail insects (Collembola). Researchers spotted surviving anoles fleeing to the trees to escape their new predator, and that triggered damage to plants. The team knew from previous work 8 that anoles adapt fairly quickly to tree climbing by favouring shorter-limbed offspring.

A curly tailed lizard (Leiocephalus carinatus). Credit: Dov Makabaw Cuba/Alamy

But then something unexpected happened. Hurricane Irene hit the islands in 2011, followed by Hurricane Sandy in 2012. Populations of both anoles and curly-tailed lizards crashed. On some islands, anoles were completely wiped out after the storm.

“The hurricanes are a mixed blessing because on the one hand, they give us all kinds of interesting data about disturbance,” Schoener says. “But on the other hand, it can slow down what might be a normal progression of evolution.”

The team has managed to keep its project on track, and is observing evolutionary changes in leg length and the lizards’ re-colonization of the islands after the hurricane.

Surprisingly, the anoles that survived the storm have longer limbs than the pre-hurricane population 7 — the opposite of the team’s prediction, but perhaps better for holding on to branches tightly during a storm. The team has just received funding to study how this evolutionary change will affect the ecosystem.

The hurricanes certainly complicated Schoener’s study, but other researchers appreciate the unplanned intervention because it provides a chance to study the consequences of real events and watch the lizards recolonize the islands. Even in the absence of a natural disaster, any number of dynamics could also change the course of an organism’s evolution, says Best. “Those potential interactions are going on for everything in the ecosystem.”

She and others say there is plenty more to do, both in the lab and in more-elaborate field studies. Some researchers want to add genetic data to their work, to understand what is driving evolution in the first place. This would tell them whether a particular trait — growth rate, for example — is truly heritable and evolving, rather than a characteristic that can be directly affected by an animal’s environment. Genomic data could also help to find hidden characteristics — those harder to observe than body size or growth rate — that might affect ecology.

In a study 9 of algae and rotifers, Lutz Becks, an evolutionary ecologist at the Max Planck Institute for Evolutionary Biology in Plön, Germany, and his colleagues watched several cycles in which populations waxed and waned as the algae clumped together and dispersed. But when the team looked at individual genes underlying clumping behaviour, they found that their expression varied wildly from one cycle to the next, even though the clumping looked the same. They have since observed co-evolution of three species at once — algae, rotifers and a virus — and found 10 that the rotifers slowed the rate at which the algae and virus co-evolved. The team plans to repeat this type of experiment, analysing genome data to see how specific details of the algal and viral genes change over time. “We’d like to get to a point where we can actually predict what genomic architecture might be needed for rapid evolution,” says Becks.

Rapid evolution can offset — at least partially — the damaging effects of climate change and other ecological disturbances. In 2011, for instance, a group led by Ellner reanalysed 11 35 years of data from dormant eggs of Daphnia water fleas, exhumed from a sediment core in Lake Constance. The data represented periods before, during and after a time when the lake was affected by blooms of cyanobacteria, a microbe with low nutritional value for Daphnia. The team found that as the Daphnia’s food became less nutritious, juvenile fleas grew poorly and ended up as smaller adults. But after several generations, evolutionary changes caused the growth rate of juveniles to return to normal. And the adults regained some of their lost stature, although they didn’t reach the same size as they had before the blooms. The researchers suggest that rapid evolution is likely to occur most often when the environment is changing, but the effects are hidden because they pull in opposite directions. “Evolution is going to be part of how the biosphere responds to climate change,” Ellner says.

Farkas has these questions about evolution and ecology at the front of his mind as he beats the bushes around Santa Barbara and sorts his stick insects. He and his team are planning even more elaborate schemes. They want to catch a full feedback cycle unfolding — ecology affecting evolution affecting ecology once more — all while collecting genetic data. “Comparing how large these effects of evolution will be and understanding when and where evolution is happening is going to be important,” says Farkas. “To me, it’s the final frontier. But it’s going to take a really long time.”

Nature 554, 19-21 (2018)


Natural Selection

You can look to Charles Darwin's seminal theory of natural selection as the main mechanism for microevolution. Alleles that produce favorable adaptations get passed to future generations because those desirable traits make it more likely that the individuals possessing them live long enough to reproduce. As a result, unfavorable adaptations eventually get bred out of the population and those alleles disappear from the gene pool. Over time, changes in allele frequency become more apparent when compared to previous generations.


What is the Specific Nature of Observable Micro-evolution - Biology

Nature of science (NOS) is a critical component of scientific literacy that enhances students’ understandings of science concepts and enables them to make informed decisions about scientifically-based personal and societal issues. NOS is derived not only from the eight science practices delineated in the Framework for K–12 Science Education (2012), but also from decades of research supporting the various forms of systematic gathering of information through direct and indirect observations of the natural world and the testing of this information by the various research methods used in science, such as descriptive, correlational, and experimental designs. All science educators and those involved with science teaching and learning should have a shared accurate view of nature of scientific knowledge, and recognize that NOS should be taught explicitly alongside science and engineering practices, disciplinary core ideas, and crosscutting concepts.

It is important to know that this new iteration of NOS improves upon the previous NSTA position statement on this topic (NSTA 2000) that used the label “nature of science,” which included a combination of characteristics of scientific knowledge (NOS) and scientific inquiry. It demonstrated the common conflation of how scientific knowledge is developed and its characteristics. Since the recent NSTA position statement on science practices, previously referred to as “inquiry” (NSTA 2018), clearly delineates how knowledge is developed in science, a more appropriate label for the focus of this position statement would be “nature of scientific knowledge” (NOSK). This would clarify the difference between how knowledge is developed from the characteristics of the resulting knowledge. Clearly the two are closely related, but they are different (Lederman & Lederman 2014). However, introducing a new label (i.e., NOSK), given that the NGSS refers to the characteristics of scientific knowledge as NOS, would create more confusion. It will be clear that the discussion of NOS here is about the characteristics of scientific knowledge. Additionally, the word “the” is removed preceding NOS to avoid implying that a single set of knowledge characteristics exists.

Why Learn About Nature of Science?

Understanding of NOS is a critical component of scientific literacy. It enhances students’ understandings of science concepts and enables them to make informed decisions about scientifically-based personal and societal issues. Although NOS has been viewed as an important educational outcome for science students for more than 100 years, it was Showalter’s (1974) work that galvanized NOS as an important construct within the overarching framework of scientific literacy. Admittedly, the phrase scientific literacy had been discussed by numerous others before Showalter (e.g., Dewey 1916 Hurd 1958 National Education Association 1918, 1920 National Society for the Study of Education 1960 among others), but it was his work that clearly delineated the dimensions of scientific literacy in a manner that could easily be translated into objectives for science curricula. NOS and science processes (now known as inquiry or practices) were clearly emphasized as equally important as “traditional” science subject matter and should also be taught explicitly, just as is done with other science subject matter (Bybee 2013). The attributes of a scientifically literate individual were later reiterated and elaborated upon by the National Science Teachers Association (NSTA 1982).

Declarations

The National Science Teaching Association endorses the proposition that science, along with its methods, explanations, and generalizations, must be the sole focus of instruction in science classes to the exclusion of all nonscientific or pseudoscientific methods, explanations, generalizations, and products.

NSTA makes the following declarations for science educators to support teaching NOS. The following premises, as well as the terminology (e.g., tentative, subjective, etc.) of nature of science, are critical and developmentally appropriate (for precollege students). They should be understood by all students by the time they graduate high school. The understandings are elaborated slightly beyond the items listed in the Next Generation Science Standards (NGSS).

  • Scientific knowledge is simultaneously reliable and subject to change. Having confidence in scientific knowledge is reasonable, while also realizing that such knowledge may be abandoned or modified in light of new evidence or a re-conceptualization of prior evidence and knowledge. The history of science reveals both evolutionary and revolutionary changes. With new evidence and interpretation, old ideas are replaced or supplemented by newer ones. Because scientific knowledge is partly the result of inference, creativity, and subjectivity, it is subject to change (AAAS 1993 Kuhn 1962).
  • Although no single universal step-by-step scientific method captures the complexity of doing science, a number of shared values and perspectives characterize a scientific approach to understanding nature. Among these are a demand for naturalistic explanations supported by empirical evidence that are, at least in principle, testable against the natural world. Other shared elements include observations, rational argument, inference, skepticism, peer review, and reproducibility of the work. This characteristic of science is also a component of the idea that “science is a way of knowing” as distinguished from other ways of knowing (Feyerabend 1975 Moore 1993 NGSS Lead States 2013).
  • In general, all scientific knowledge is a combination of observations and inferences (Chalmers 1999 Gould 1981). For example, students of all ages pay attention to weather forecasts. Weather forecasters make observations, and their forecasts are inferences. All science textbooks have a picture of the atom, but the picture is really an inference from observable data of how matter behaves.
  • Creativity is a vital, yet personal, ingredient in the production of scientific knowledge. It is a component of science as a human endeavor (Bronowski 1956 Hoffman & Torrence 1993 Kuhn 1962).
  • Subjectivity is an unavoidable aspect of scientific knowledge. Because “science is a human endeavor,” it is subject to the functions of individual human thinking and perceptions. Although objectivity is always desired in the interpretation of data, some subjectivity is unavoidable and often beneficial (Chalmers 1999 Gould 1981 Laudan 1977).
  • Science, by definition, is limited to naturalistic methods and explanations, and as such, is precluded from using supernatural elements in the production of scientific knowledge. This is a component of the recognition that scientific knowledge is empirically based (Hoffman & Torrence 1993).
  • A primary goal of science is the formation of theories and laws, which are terms with very specific meanings:
    1. Laws are generalizations or universal relationships related to the way that some aspect of the natural world behaves under certain conditions. They describe relationships among what has been observed in the natural world. For example, Boyle’s Law describes the relationship between pressure and volume of a gas at a constant temperature (Feynman 1965 Harre 1983 National Academy of Sciences 1998).
    2. Theories are inferred explanations of some aspect of the natural world. They provide explanations for what has been stated in scientific laws. Theories do not become laws even with additional evidence they explain laws. However, not all scientific laws have accompanying explanatory theories (Feynman 1965 Harre 1983 Mayr 1988 National Academy of Sciences 1998 Ruse 1998).
    3. Well-established laws and theories must
      • be internally consistent and compatible with the best available evidence
      • be successfully tested against a wide range of applicable phenomena and evidence and
      • possess appropriately broad and demonstrable effectiveness in further research (Kuhn 1962 Lakatos 1983 Popper 1968).

      These premises combined provide the foundation for how scientific knowledge is formed and are foundational to nature of science. The NGSS (2013) lists the following eight components of NOS. Given the previous discussion about the differences between how knowledge is developed and what is done with that knowledge as scientific practice, items 1, 5, and 6 are arguably more aligned with science practices (or inquiry) than characteristics of scientific knowledge. Practices and knowledge are obviously entangled in the real world and in classroom instruction, yet it is important for teachers of science to know the difference between science practices and the characteristics of scientific knowledge to best lead students to a comprehensive understanding of nature of science. Items 5 and 7 are a bit vague for concrete use in K–12 classrooms. Consequently, a more concrete discussion of what these items mean was provided in the previous section.

      NSTA recommends that by the time they graduate from high school, students should understand the following concepts related to NOS:

      • Scientific Investigations Use a Variety of Methods
      • Scientific Knowledge Is Based on Empirical Evidence
      • Scientific Knowledge Is Open to Revision in Light of New Evidence
      • Science Models, Laws, Mechanisms, and Theories Explain Natural Phenomena
      • Science Is a Way of Knowing
      • Scientific Knowledge Assumes an Order and Consistency in Natural Systems
      • Science Is a Human Endeavor and
      • Science Addresses Questions About the Natural and Material World.

      Concluding Remarks

      NOS (i.e., the characteristics of scientific knowledge as derived from how it is produced) has long been recognized as a critical component of scientific literacy. It is necessary knowledge for students to make informed decisions with respect to the ever-increasing scientifically-based personal and societal issues. The research clearly indicates that for students to learn about NOS, it must be planned for and assessed just like any of the instructional goals focusing on science and engineering practices, disciplinary core ideas, and crosscutting concepts (Lederman 2007 Lederman & Lederman 2014). It is not learned by chance, simply by doing science. NOS is best understood by students if it is explicitly addressed within the context of students’ learning of science and engineering practices, disciplinary core ideas, and crosscutting concepts. “Explicit” does not mean that the teacher should lecture about NOS. Rather, it refers to reflective discussions among students about the science concepts they are learning (Clough 2011).All aspects of NOS cannot and should not be taught in a single lesson, nor are all aspects developmentally appropriate for all grade levels. For example, understandings of the differences between theories and laws or the cultural embeddedness of science are not developmentally appropriate for K–5 students. Nevertheless, NOS should be included at all grade levels as a unifying theme for the K–12 science curriculum. All too often, NOS is only taught explicitly at the beginning of a science course, independent of any of the science content that will subsequently follow. Instead, NOS should be taught as a unifying theme with the expectation that students’ knowledge will progressively become more and more sophisticated as they progress through the K–12 curriculum.

      —Adopted by the NSTA Board of Directors, January 2020

      Research and Theoretical References

      Abd-El-Khalick, F., and N.G. Lederman. 2000. Improving science teachers’ conceptions of the nature of science: A critical review of the literature. International Journal of Science Education 22 (7): 665–701.

      American Association for the Advancement of Science (AAAS). 1993. Benchmarks for science literacy. New York: Oxford University Press.

      Bronowski, J. 1956. Science and human values. New York: Harper & Row Publishers, Inc.

      Bybee, R.W. 2013. Translating the NGSS for classroom imstruction. Arlington, VA: NSTA Press.

      Chalmers, A.F. 1999. What is this thing called science? Queensland, AU: University of Queensland Press.

      Dewey, J. 1916. Democracy and education. New York: The Free Press.

      Feyerabend, P.F. 1975. Against method: Outline of an anarchistic theory of knowledge. Great Britain: Redwood, Burn Limited.

      Feynman, R.P. 1965. The character of physical law. Cambridge, MA: MIT Press.

      Gould, S.J. 1981. The mismeasure of man. New York: W.W. Norton & Company.

      Hoffman, R., and V. Torrence. 1993. Chemistry imagined: Reflections on science. Washington, DC: Smithsonian Institution Press.

      Hurd, P.D. 1958. Science literacy: 16 (1): 13–16.

      Kuhn, T.S. 1962. The structure of scientific revolutions. Chicago: The University of Chicago Press.

      Lakatos, I. 1983. Mathematics, science, and epistemology. Cambridge, UK: Cambridge University Press.

      Laudan, L. 1977. Progress and its problems: Towards a theory of scientific growth. Berkeley, CA: University of California Press.

      Lederman, N.G. 2007. Nature of science: Past, present, and future. In Handbook of research on science education, ed. S.K. Abell and N.G. Lederman, 831–880. Mahwah, NJ: Lawrence Erlbaum Associates.

      Lederman, N.G., and J.S. Lederman. 2014. Research on teaching and learning of nature of science. In Handbook of research on science education, Volume II, ed. N.G. Lederman and S.K. Abell, 600–620. New York: Routledge.

      Mayr, E. 1988. Toward a new philosophy in biology. Cambridge, MA: Harvard University Press.

      Moore, J. 1993. Science as a way of knowing: The foundation of modern biology. Cambridge, MA: Harvard University Press.

      National Education Association. 1918. Cardinal principles of secondary education: A report of the commission on the reorganization of secondary education. (U.S. Bureau of Education Bulletin No. 35). Washington, DC: U.S. Government Printing Office.

      National Education Association. 1920. Reorganization of science in secondary schools: A report of the commission on the reorganization of secondary education. (U.S. Bureau of Education Bulletin No. 20). Washington, DC: U.S. Government Printing Office.

      National Research Council (NRC). 2012. A framework for K–12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: National Academies Press. National Science Teachers Association. 1982. Science-technology-society: Science education for the 1980s. Washington, DC: Author.

      National Science Teachers Association. 2018. Transitioning from scientific inquiry to three-dimensional teaching and learning. Arlington, VA: Author.

      National Science Teachers Association. 2000. The nature of science: NSTA Position Statement. Arlington, VA: Author.

      National Society for the Study of Education. 1960. Rethinking Science Education: Yearbook of the National Society for the Study of Education. Chicago: University of Chicago Press 59: 113.

      NGSS Lead States. 2013. Next generation science standards: For states, by states. Washington, DC: National Academies Press. www.nextgenscience.org/next-generation-science-standards.

      Popper, K.R. 1968. The logic of scientific discovery. New York: Harper & Row Publishers.

      Ruse, M. (Ed.) 1998. Philosophy of biology. New York: Prometheus Books.

      Showalter, V.M. 1974. What is unified science education? Program objectives and scientific literacy. Prism 2(3–4): 1–6.

      References of Teaching Resources

      Bell, R.L. 2008. Teaching the nature of science through process skills: Activities for grades 3–8. New York: Pearson.

      Clough, M.P. 2011. Teaching and assessing the nature of science: How to effectively incorporate the nature of science in your classroom. The Science Teacher 78 (6): 56–60

      Clough, M.P., and J.K. Olson. 2004. The nature of science: Always part of the science story. The Science Teacher 71 (9): 28–31.

      Lederman, N.G., and F. Abd-El-Khalick. 1998. Avoiding de-natured science: Activities that promote understandings of the nature of science. In The nature of science in science education: Rationales and strategies, ed. W.F. McComas, 83–126. The Netherlands: Kluwer Academic Publishers.

      McComas, W.F., ed. 2019. Nature of science in science instruction: Rationales and strategies. Dordrecht, The Netherlands: Springer Publishing.

      National Academy of Sciences. 1998. Teaching about evolution and the nature of science. Washington, DC: National Academies Press.


      3. Theory and value ladenness

      Empirical results are laden with values and theoretical commitments. Philosophers have raised and appraised several possible kinds of epistemic problems that could be associated with theory and/or value-laden empirical results. They have worried about the extent to which human perception itself is distorted by our commitments. They have worried that drawing upon theoretical resources from the very theory to be appraised (or its competitors) in the generation of empirical results yields vicious circularity (or inconsistency). They have also worried that contingent conceptual and/or linguistic frameworks trap bits of evidence like bees in amber so that they cannot carry on their epistemic lives outside of the contexts of their origination, and that normative values necessarily corrupt the integrity of science. Do the theory and value-ladenness of empirical results render them hopelessly parochial? That is, when scientists leave theoretical commitments behind and adopt new ones, must they also relinquish the fruits of the empirical research imbued with their prior commitments too? In this section, we discuss these worries and responses that philosophers have offered to assuage them.

      3.1 Perception

      If you believe that observation by human sense perception is the objective basis of all scientific knowledge, then you ought to be particularly worried about the potential for human perception to be corrupted by theoretical assumptions, wishful thinking, framing effects, and so on. Daston and Galison recount the striking example of Arthur Worthington&rsquos symmetrical milk drops (2007, 11&ndash16). Working in 1875, Worthington investigated the hydrodynamics of falling fluid droplets and their evolution upon impacting a hard surface. At first, he had tried to carefully track the drop dynamics with a strobe light to burn a sequence of images into his own retinas. The images he drew to record what he saw were radially symmetric, with rays of the drop splashes emanating evenly from the center of the impact. However, when Worthington transitioned from using his eyes and capacity to draw from memory to using photography in 1894, he was shocked to find that the kind of splashes he had been observing were irregular splats (ibid., 13). Even curiouser, when Worthington returned to his drawings, he found that he had indeed recorded some unsymmetrical splashes. He had evidently dismissed them as uninformative accidents instead of regarding them as revelatory of the phenomenon he was intent on studying (ibid.) In attempting to document the ideal form of the splashes, a general and regular form, he had subconsciously down-played the irregularity of individual splashes. If theoretical commitments, like Worthington&rsquos initial commitment to the perfect symmetry of the physics he was studying, pervasively and incorrigibly dictated the results of empirical inquiry, then the epistemic aims of science would be seriously undermined.

      Perceptual psychologists, Bruner and Postman, found that subjects who were briefly shown anomalous playing cards, e.g., a black four of hearts, reported having seen their normal counterparts e.g., a red four of hearts. It took repeated exposures to get subjects to say the anomalous cards didn&rsquot look right, and eventually, to describe them correctly (Kuhn 1962, 63). Kuhn took such studies to indicate that things don&rsquot look the same to observers with different conceptual resources. (For a more up-to-date discussion of theory and conceptual perceptual loading see Lupyan 2015.) If so, black hearts didn&rsquot look like black hearts until repeated exposures somehow allowed subjects to acquire the concept of a black heart. By analogy, Kuhn supposed, when observers working in conflicting paradigms look at the same thing, their conceptual limitations should keep them from having the same visual experiences (Kuhn 1962, 111, 113&ndash114, 115, 120&ndash1). This would mean, for example, that when Priestley and Lavoisier watched the same experiment, Lavoisier should have seen what accorded with his theory that combustion and respiration are oxidation processes, while Priestley&rsquos visual experiences should have agreed with his theory that burning and respiration are processes of phlogiston release.

      The example of Pettersson&rsquos and Rutherford&rsquos scintillation screen evidence (above) attests to the fact that observers working in different laboratories sometimes report seeing different things under similar conditions. It is plausible that their expectations influence their reports. It is plausible that their expectations are shaped by their training and by their supervisors&rsquo and associates&rsquo theory driven behavior. But as happens in other cases as well, all parties to the dispute agreed to reject Pettersson&rsquos data by appealing to results that both laboratories could obtain and interpret in the same way without compromising their theoretical commitments. Indeed, it is possible for scientists to share empirical results, not just across diverse laboratory cultures, but even across serious differences in worldview. Much as they disagreed about the nature of respiration and combustion, Priestley and Lavoisier gave quantitatively similar reports of how long their mice stayed alive and their candles kept burning in closed bell jars. Priestley taught Lavoisier how to obtain what he took to be measurements of the phlogiston content of an unknown gas. A sample of the gas to be tested is run into a graduated tube filled with water and inverted over a water bath. After noting the height of the water remaining in the tube, the observer adds &ldquonitrous air&rdquo (we call it nitric oxide) and checks the water level again. Priestley, who thought there was no such thing as oxygen, believed the change in water level indicated how much phlogiston the gas contained. Lavoisier reported observing the same water levels as Priestley even after he abandoned phlogiston theory and became convinced that changes in water level indicated free oxygen content (Conant 1957, 74&ndash109).

      A related issue is that of salience. Kuhn claimed that if Galileo and an Aristotelian physicist had watched the same pendulum experiment, they would not have looked at or attended to the same things. The Aristotelian&rsquos paradigm would have required the experimenter to measure

      and ignore radius, angular displacement, and time per swing (ibid., 124). These last were salient to Galileo because he treated pendulum swings as constrained circular motions. The Galilean quantities would be of no interest to an Aristotelian who treats the stone as falling under constraint toward the center of the earth (ibid., 123). Thus Galileo and the Aristotelian would not have collected the same data. (Absent records of Aristotelian pendulum experiments we can think of this as a thought experiment.)

      Interests change, however. Scientists may eventually come to appreciate the significance of data that had not originally been salient to them in light of new presuppositions. The moral of these examples is that although paradigms or theoretical commitments sometimes have an epistemically significant influence on what observers perceive or what they attend to, it can be relatively easy to nullify or correct for their effects. When presuppositions cause epistemic damage, investigators are often able to eventually make corrections. Thus, paradigms and theoretical commitments actually do influence saliency, but their influence is neither inevitable nor irremediable.

      3.2 Assuming the theory to be tested

      Thomas Kuhn (1962), Norwood Hanson (1958), Paul Feyerabend (1959) and others cast suspicion on the objectivity of observational evidence in another way by arguing that one cannot use empirical evidence to test a theory without committing oneself to that very theory. This would be a problem if it leads to dogmatism but assuming the theory to be tested is often benign and even necessary.

      For instance, Laymon (1988) demonstrates the manner in which the very theory that the Michelson-Morley experiments are considered to test is assumed in the experimental design, but that this does not engender deleterious epistemic effects (250). The Michelson-Morley apparatus consists of two interferometer arms at right angles to one another, which are rotated in the course of the experiment so that, on the original construal, the path length traversed by light in the apparatus would vary according to alignment with or against the Earth&rsquos velocity (carrying the apparatus) with respect to the stationary aether. This difference in path length would show up as displacement in the interference fringes of light in the interferometer. Although Michelson&rsquos intention had been to measure the velocity of the Earth with respect to the all-pervading aether, the experiments eventually came to be regarded as furnishing tests of the Fresnel aether theory itself. In particular, the null results of these experiments were taken as evidence against the existence of the aether. Naively, one might suppose that whatever assumptions were made in the calculation of the results of these experiments, it should not be the case that the theory under the gun was assumed nor that its negation was.

      Before Michelson&rsquos experiments, the Fresnel aether theory did not predict any sort of length contraction. Although Michelson assumed no contraction in the arms of the interferometer, Laymon argues that he could have assumed contraction, with no practical impact on the results of the experiments. The predicted fringe shift is calculated from the anticipated difference in the distance traveled by light in the two arms is the same, when higher order terms are neglected. Thus, in practice, the experimenters could assume either that the contraction thesis was true or that it was false when determining the length of the arms. Either way, the results of the experiment would be the same. After Michelson&rsquos experiments returned no evidence of the anticipated aether effects, Lorentz-Fitzgerald contraction was postulated precisely to cancel out the expected (but not found) effects and save the aether theory. Morley and Miller then set out specifically to test the contraction thesis, and still assumed no contraction in determining the length of the arms of their interferometer (ibid., 253). Thus Laymon argues that the Michelson-Morley experiments speak against the tempting assumption that &ldquoappraisal of a theory is based on phenomena which can be detected and measured without using assumptions drawn from the theory under examination or from competitors to that theory&rdquo (ibid., 246).

      Epistemological hand-wringing about the use of the very theory to be tested in the generation of the evidence to be used for testing, seems to spring primarily from a concern about vicious circularity. How can we have a genuine trial, if the theory in question has been presumed innocent from the outset? While it is true that there would be a serious epistemic problem in a case where the use of the theory to be tested conspired to guarantee that the evidence would turn out to be confirmatory, this is not always the case when theories are invoked in their own testing. Woodward (2011) summarizes a tidy case:

      For any given case, determining whether the theoretical assumptions being made are benign or straight-jacketing the results that it will be possible to obtain will require investigating the particular relationships between the assumptions and results in that case. When data production and analysis processes are complicated, this task can get difficult. But the point is that merely noting the involvement of the theory to be tested in the generation of empirical results does not by itself imply that those results cannot be objectively useful for deciding whether the theory to be tested should be accepted or rejected.

      3.3 Semantics

      Kuhn argued that theoretical commitments exert a strong influence on observation descriptions, and what they are understood to mean (Kuhn 1962, 127ff Longino 1979, 38&ndash42). If so, proponents of a caloric account of heat won&rsquot describe or understand descriptions of observed results of heat experiments in the same way as investigators who think of heat in terms of mean kinetic energy or radiation. They might all use the same words (e.g., &lsquotemperature&rsquo) to report an observation without understanding them in the same way. This poses a potential problem for communicating effectively across paradigms, and similarly, for attributing the appropriate significance to empirical results generated outside of one&rsquos own linguistic framework.

      It is important to bear in mind that observers do not always use declarative sentences to report observational and experimental results. Instead, they often draw, photograph, make audio recordings, etc. or set up their experimental devices to generate graphs, pictorial images, tables of numbers, and other non-sentential records. Obviously investigators&rsquo conceptual resources and theoretical biases can exert epistemically significant influences on what they record (or set their equipment to record), which details they include or emphasize, and which forms of representation they choose (Daston and Galison 2007, 115&ndash190, 309&ndash361). But disagreements about the epistemic import of a graph, picture or other non-sentential bit of data often turn on causal rather than semantical considerations. Anatomists may have to decide whether a dark spot in a micrograph was caused by a staining artifact or by light reflected from an anatomically significant structure. Physicists may wonder whether a blip in a Geiger counter record reflects the causal influence of the radiation they wanted to monitor, or a surge in ambient radiation. Chemists may worry about the purity of samples used to obtain data. Such questions are not, and are not well represented as, semantic questions to which semantic theory loading is relevant. Late 20 th century philosophers may have ignored such cases and exaggerated the influence of semantic theory loading because they thought of theory testing in terms of inferential relations between observation and theoretical sentences.

      Nevertheless, some empirical results are reported as declarative sentences. Looking at a patient with red spots and a fever, an investigator might report having seen the spots, or measles symptoms, or a patient with measles. Watching an unknown liquid dripping into a litmus solution an observer might report seeing a change in color, a liquid with a PH of less than 7, or an acid. The appropriateness of a description of a test outcome depends on how the relevant concepts are operationalized. What justifies an observer to report having observed a case of measles according to one operationalization might require her to say no more than that she had observed measles symptoms, or just red spots according to another.

      In keeping with Percy Bridgman&rsquos view that

      one might suppose that operationalizations are definitions or meaning rules such that it is analytically true, e.g., that every liquid that turns litmus red in a properly conducted test is acidic. But it is more faithful to actual scientific practice to think of operationalizations as defeasible rules for the application of a concept such that both the rules and their applications are subject to revision on the basis of new empirical or theoretical developments. So understood, to operationalize is to adopt verbal and related practices for the purpose of enabling scientists to do their work. Operationalizations are thus sensitive and subject to change on the basis of findings that influence their usefulness (Feest 2005).

      Definitional or not, investigators in different research traditions may be trained to report their observations in conformity with conflicting operationalizations. Thus instead of training observers to describe what they see in a bubble chamber as a whitish streak or a trail, one might train them to say they see a particle track or even a particle. This may reflect what Kuhn meant by suggesting that some observers might be justified or even required to describe themselves as having seen oxygen, transparent and colorless though it is, or atoms, invisible though they are (Kuhn 1962, 127ff). To the contrary, one might object that what one sees should not be confused with what one is trained to say when one sees it, and therefore that talking about seeing a colorless gas or an invisible particle may be nothing more than a picturesque way of talking about what certain operationalizations entitle observers to say. Strictly speaking, the objection concludes, the term &lsquoobservation report&rsquo should be reserved for descriptions that are neutral with respect to conflicting operationalizations.

      If observational data are just those utterances that meet Feyerabend&rsquos decidability and agreeability conditions, the import of semantic theory loading depends upon how quickly, and for which sentences reasonably sophisticated language users who stand in different paradigms can non-inferentially reach the same decisions about what to assert or deny. Some would expect enough agreement to secure the objectivity of observational data. Others would not. Still others would try to supply different standards for objectivity.

      With regard to sentential observation reports, the significance of semantic theory loading is less ubiquitous than one might expect. The interpretation of verbal reports often depends on ideas about causal structure rather than the meanings of signs. Rather than worrying about the meaning of words used to describe their observations, scientists are more likely to wonder whether the observers made up or withheld information, whether one or more details were artifacts of observation conditions, whether the specimens were atypical, and so on.

      Note that the worry about semantic theory loading extends beyond observation reports of the sort that occupied the logical empiricists and their close intellectual descendents. Combining results of diverse methods for making proxy measurements of paleoclimate temperatures in an epistemically responsible way requires careful attention to the variety of operationalizations at play. Even if no &lsquoobservation reports&rsquo are involved, the sticky question about how to usefully merge results obtained in different ways in order to satisfy one&rsquos epistemic aims remains. Happily, the remedy for the worry about semantic loading in this broader sense is likely to be the same&mdashinvestigating the provenance of those results and comparing the variety of factors that have contributed to their causal production.

      Kuhn placed too much emphasis on the discontinuity between evidence generated in different paradigms. Even if we accept a broadly Kuhnian picture, according to which paradigms are heterogeneous collections of experimental practices, theoretical principles, problems selected for investigation, approaches to their solution, etc., connections between components are loose enough to allow investigators who disagree profoundly over one or more theoretical claims to nevertheless agree about how to design, execute, and record the results of their experiments. That is why neuroscientists who disagreed about whether nerve impulses consisted of electrical currents could measure the same electrical quantities, and agree on the linguistic meaning and the accuracy of observation reports including such terms as &lsquopotential&rsquo, &lsquoresistance&rsquo, &lsquovoltage&rsquo and &lsquocurrent&rsquo. As we discussed above, the success that scientists have in repurposing results generated by others for different purposes speaks against the confinement of evidence to its native paradigm. Even when scientists working with radically different core theoretical commitments cannot make the same measurements themselves, with enough contextual information about how each conducts research, it can be possible to construct bridges that span the theoretical divides.

      3.4 Values

      One could worry that the intertwining of the theoretical and empirical would open the floodgates to bias in science. Human cognizing, both historical and present day, is replete with disturbing commitments including intolerance and narrow mindedness of many sorts. If such commitments are integral to a theoretical framework, or endemic to the reasoning of a scientist or scientific community, then they threaten to corrupt the epistemic utility of empirical results generated using their resources. The core impetus of the &lsquovalue-free ideal&rsquo is to maintain a safe distance between the appraisal of scientific theories according to the evidence on one hand, and the swarm of moral, political, social, and economic values on the other. While proponents of the value-free ideal might admit that the motivation to pursue a theory or the legal protection of human subjects in permissible experimental methods involve non-epistemic values, they would contend that such values ought not ought not enter into the constitution of empirical results themselves, nor the adjudication or justification of scientific theorizing in light of the evidence (see Intemann 2021, 202).

      As a matter of fact, values do enter into science at a variety of stages. Above we saw that &lsquotheory-ladenness&rsquo could refer to the involvement of theory in perception, in semantics, and in a kind of circularity that some have worried begets unfalsifiability and thereby dogmatism. Like theory-ladenness, values can and sometimes do affect judgments about the salience of certain evidence and the conceptual framing of data. Indeed, on a permissive construal of the nature of theories, values can simply be understood as part of a theoretical framework. Intemann (2021) highlights a striking example from medical research where key conceptual resources include notions like &lsquoharm,&rsquo &lsquorisk,&rsquo &lsquohealth benefit,&rsquo and &lsquosafety.&rsquo She refers to research on the comparative safety of giving birth at home and giving birth at a hospital for low-risk parents in the United States. Studies reporting that home births are less safe typically attend to infant and birthing parent mortality rates&mdashwhich are low for these subjects whether at home or in hospital&mdashbut leave out of consideration rates of c-section and episiotomy, which are both relatively high in hospital settings. Thus, a value-laden decision about whether a possible outcome counts as a harm worth considering can influence the outcome of the study&mdashin this case tipping the balance towards the conclusion that hospital births are more safe (ibid., 206).

      Note that the birth safety case differs from the sort of cases at issue in the philosophical debate about risk and thresholds for acceptance and rejection of hypotheses. In accepting an hypothesis, a person makes a judgement that the risk of being mistaken is sufficiently low (Rudner 1953). When the consequences of being wrong are deemed grave, the threshold for acceptance may be correspondingly high. Thus, in evaluating the epistemic status of an hypothesis in light of the evidence, a person may have to make a value-based judgement. However, in the birth safety case, the judgement comes into play at an earlier stage, well before the decision to accept or reject the hypothesis is to be made. The judgement occurs already in deciding what is to count as a &lsquoharm&rsquo worth considering for the purposes of this research.

      The fact that values do sometimes enter into scientific reasoning does not by itself settle the question of whether it would be better if they did not. In order to assess the normative proposal, philosophers of science have attempted to disambiguate the various ways in which values might be thought to enter into science, and the various referents that get crammed under the single heading of &lsquovalues.&rsquo Anderson (2004) articulates eight stages of scientific research where values (&lsquoevaluative presuppositions&rsquo) might be employed in epistemically fruitful ways. In paraphrase: 1) orientation in a field, 2) framing a research question, 3) conceptualizing the target, 4) identifying relevant data, 5) data generation, 6) data analysis, 7) deciding when to cease data analysis, and 8) drawing conclusions (Anderson 2004, 11). Similarly, Intemann (2021) lays out five ways &ldquothat values play a role in scientific reasoning&rdquo with which feminist philosophers of science have engaged in particular:

      Ward (2021) presents a streamlined and general taxonomy of four ways in which values relate to choices: as reasons motivating or justifying choices, as causal effectors of choices, or as goods affected by choices. By investigating the role of values in these particular stages or aspects of research, philosophers of science can offer higher resolution insights than just the observation that values are involved in science at all and untangle crosstalk.

      Similarly, fine points can be made about the nature of values involved in these various contexts. Such clarification is likely important for determining whether the contribution of certain values in a given context is deleterious or salutary, and in what sense. Douglas (2013) argues that the &lsquovalue&rsquo of internal consistency of a theory and of the empirical adequacy of a theory with respect to the available evidence are minimal criteria for any viable scientific theory (799&ndash800). She contrasts these with the sort of values that Kuhn called &lsquovirtues,&rsquo i.e. scope, simplicity, and explanatory power that are properties of theories themselves, and unification, novel prediction and precision, which are properties a theory has in relation to a body of evidence (800&ndash801). These are the sort of values that may be relevant to explaining and justifying choices that scientists make to pursue/abandon or accept/reject particular theories. Moreover, Douglas (2000) argues that what she calls &ldquonon-epistemic values&rdquo (in particular, ethical value judgements) also enter into decisions at various stages &ldquointernal&rdquo to scientific reasoning, such as data collection and interpretation (565). Consider a laboratory toxicology study in which animals exposed to dioxins are compared to unexposed controls. Douglas discusses researchers who want to determine the threshold for safe exposure. Admitting false positives can be expected to lead to overregulation of the chemical industry, while false negatives yield underregulation and thus pose greater risk to public health. The decision about where to set the unsafe exposure threshold, that is, set the threshold for a statistically significant difference between experimental and control animal populations, involves balancing the acceptability of these two types of errors. According to Douglas, this balancing act will depend on &ldquowhether we are more concerned about protecting public health from dioxin pollution or whether we are more concerned about protecting industries that produce dioxins from increased regulation&rdquo (ibid., 568). That scientists do as a matter of fact sometimes make such decisions is clear. They judge, for instance, a specimen slide of a rat liver to be tumorous or not, and whether borderline cases should count as benign or malignant (ibid., 569&ndash572). Moreover, in such cases, it is not clear that the responsibility of making such decisions could be offloaded to non-scientists.

      Many philosophers accept that values can contribute to the generation of empirical results without spoiling their epistemic utility. Anderson&rsquos (2004) diagnosis is as follows:

      Data production (including experimental design and execution) is heavily influenced by investigators&rsquo background assumptions. Sometimes these include theoretical commitments that lead experimentalists to produce non-illuminating or misleading evidence. In other cases they may lead experimentalists to ignore, or even fail to produce useful evidence. For example, in order to obtain data on orgasms in female stumptail macaques, one researcher wired up females to produce radio records of orgasmic muscle contractions, heart rate increases, etc. But as Elisabeth Lloyd reports, &ldquo&hellip the researcher &hellip wired up the heart rate of the male macaques as the signal to start recording the female orgasms. When I pointed out that the vast majority of female stumptail orgasms occurred during sex among the females alone, he replied that yes he knew that, but he was only interested in important orgasms&rdquo (Lloyd 1993, 142). Although female stumptail orgasms occurring during sex with males are atypical, the experimental design was driven by the assumption that what makes features of female sexuality worth studying is their contribution to reproduction (ibid., 139). This assumption influenced experimental design in such a way as to preclude learning about the full range of female stumptail orgasms.

      Anderson (2004) presents an influential analysis of the role of values in research on divorce. Researchers committed to an interpretive framework rooted in &lsquotraditional family values&rsquo could conduct research on the assumption that divorce is mostly bad for spouses and any children that they have (ibid., 12). This background assumption, which is rooted in a normative appraisal of a certain model of good family life, could lead social science researchers to restrict the questions with which they survey their research subjects to ones about the negative impacts of divorce on their lives, thereby curtailing the possibility of discovering ways that divorce may have actually made the ex-spouses lives better (ibid., 13). This is an example of the influence that values can have on the nature of the results that research ultimately yields, which is epistemically detrimental. In this case, the values in play biased the research outcomes to preclude recognition of countervailing evidence. Anderson argues that the problematic influence of values comes when research &ldquois rigged in advance&rdquo to confirm certain hypotheses&mdashwhen the influence of values amounts to incorrigible dogmatism (ibid., 19). &ldquoDogmatism&rdquo in her sense is unfalsifiability in practice, &ldquotheir stubbornness in the face of any conceivable evidence&rdquo(ibid., 22).

      Fortunately, such dogmatism is not ubiquitous and when it occurs it can often be corrected eventually. Above we noted that the mere involvement of the theory to be tested in the generation of an empirical result does not automatically yield vicious circularity&mdashit depends on how the theory is involved. Furthermore, even if the assumptions initially made in the generation of empirical results are incorrect, future scientists will have opportunities to reassess those assumptions in light of new information and techniques. Thus, as long as scientists continue their work there need be no time at which the epistemic value of an empirical result can be established once and for all. This should come as no surprise to anyone who is aware that science is fallible, but it is no grounds for skepticism. It can be perfectly reasonable to trust the evidence available at present even though it is logically possible for epistemic troubles to arise in the future. A similar point can be made regarding values (although cf. Yap 2016).

      Moreover, while the inclusion of values in the generation of an empirical result can sometimes be epistemically bad, values properly deployed can also be harmless, or even epistemically helpful. As in the cases of research on female stumptail macaque orgasms and the effects of divorce, certain values can sometimes serve to illuminate the way in which other epistemically problematic assumptions have hindered potential scientific insight. By valuing knowledge about female sexuality beyond its role in reproduction, scientists can recognize the narrowness of an approach that only conceives of female sexuality insofar as it relates to reproduction. By questioning the absolute value of one traditional ideal for flourishing families, researchers can garner evidence that might end up destabilizing the empirical foundation supporting that ideal.

      3.5 Reuse

      Empirical results are most obviously put to epistemic work in their contexts of origin. Scientists conceive of empirical research, collect and analyze the relevant data, and then bring the results to bear on the theoretical issues that inspired the research in the first place. However, philosophers have also discussed ways in which empirical results are transferred out of their native contexts and applied in diverse and sometimes unexpected ways (see Leonelli and Tempini 2020). Cases of reuse, or repurposing of empirical results in different epistemic contexts raise several interesting issues for philosophers of science. For one, such cases challenge the assumption that theory (and value) ladenness confines the epistemic utility of empirical results to a particular conceptual framework. Ancient Babylonian eclipse records inscribed on cuneiform tablets have been used to generate constraints on contemporary geophysical theorizing about the causes of the lengthening of the day on Earth (Stephenson, Morrison, and Hohenkerk 2016). This is surprising since the ancient observations were originally recorded for the purpose of making astrological prognostications. Nevertheless, with enough background information, the records as inscribed can be translated, the layers of assumptions baked into their presentation peeled back, and the results repurposed using resources of the contemporary epistemic context, the likes of which the Babylonians could have hardly dreamed.

      Furthermore, the potential for reuse and repurposing feeds back on the methodological norms of data production and handling. In light of the difficulty of reusing or repurposing data without sufficient background information about the original context, Goodman et al. (2014) note that &ldquodata reuse is most possible when: 1) data 2) metadata (information describing the data) and 3) information about the process of generating those data, such as code, all all provided&rdquo (3). Indeed, they advocate for sharing data and code in addition to results customarily published in science. As we have seen, the loading of data with theory is usually necessary to putting that data to any serious epistemic use&mdashtheory-loading makes theory appraisal possible. Philosophers have begun to appreciate that this epistemic boon does not necessarily come at the cost of rendering data &ldquotragically local&rdquo (Wylie 2020, 285, quoting Latour 1999). But it is important to note the useful travel of data between contexts is significantly aided by foresight, curation, and management for that aim.

      In light of the mediated nature of empirical results, Boyd (2018) argues for an &ldquoenriched view of evidence,&rdquo in which the evidence that serves as the &lsquotribunal of experience&rsquo is understood to be &ldquolines of evidence&rdquo composed of the products of data collection and all of the products of their transformation on the way to the generation of empirical results that are ultimately compared to theoretical predictions, considered together with metadata associated with their provenance. Such metadata includes information about theoretical assumptions that are made in data collection, processing, and the presentation of empirical results. Boyd argues that by appealing to metadata to &lsquorewind&rsquo the processing of assumption-imbued empirical results and then by re-processing them using new resources, the epistemic utility of empirical evidence can survive transitions to new contexts. Thus, the enriched view of evidence supports the idea that it is not despite the intertwining of the theoretical and empirical that scientists accomplish key epistemic aims, but often in virtue of it (ibid., 420). In addition, it makes the epistemic value of metadata encoding the various assumptions that have been made throughout the course of data collection and processing explicit.

      The desirability of explicitly furnishing empirical data and results with auxiliary information that allow them to travel can be appreciated in light of the &lsquoobjectivity&rsquo norm, construed as accessibility to interpersonal scrutiny. When data are repurposed in novel contexts, they are not only shared between subjects, but can in some cases be shared across radically different paradigms with incompatible theoretical commitments.


      What is the Specific Nature of Observable Micro-evolution - Biology

      Positive selection is the process by which new advantageous genetic variants sweep a population. Though positive selection, also known as Darwinian selection, is the main mechanism that Darwin envisioned as giving rise to evolution, specific molecular genetic examples are very difficult to detect. Pioneering work by Yang and Nielsen has provided a much more powerful methodology for detecting positive selection at the sequence level. To understand

      Based largely on a brilliant series of papers by Kimura in the 1960s and 70s, the neutral model of evolution has become the standard against which positive selection must be detected. In the neutral model, the vast majority of mutations are divided into two groups. The first group, for which the model is named, is selectively neutral (or nearly neutral) mutations which become fixed in a species by genetic drift. These changes account for almost all the observable nucleotide changes between two species. The second group is selectively deleterious mutations, which arise continuously and are eliminated over time by natural selection. Because these mutations are eventually eliminated from a species, they are rarely observed when comparing the genomes of two species. On the other hand, they are the basis a substantial fraction of population diversity within a species. Because they cause mutant phenotypes, these mutations are well known to functional geneticists, since they account for nearly all of the mutant strains and human diseases that are much studied throughout biology and human health.

      Though advantageous mutations are of great interest, they are difficult to detect and analyze because of the fact that neutral and deleterious mutations predominate them in frequency. Two major classes of methods are currently in use to detect positive selection: population methods, based on analyzing the nature and frequency of allele diversity within a species, and codon analysis methods, based on comparing patterns of synonymous and nonsynonymous changes in protein coding sequences. Unfortunately, a nearly complete lack of population sequence information in nematodes (at least for now), limits our analysis to the latter methods.

      A Simple Primer on codon based methods for detecting selection

      The essence of this method is easy to state and very difficult to implement. Protein codons have fortuitous properties that make it uniquely feasible to detect patterns of neutral mutations, deleterious mutations, and advantageous mutations. The simplest version of those patterns can be seen by considering the codon for a single amino acid in a hypothetically large number of related species (the same codon position in the same - orthologous - gene) . This codon in each of these related species is identical by descent from a single codon present in their last common ancestor. To simplify, consider that we know the ancestral codon is ACT, which codes for threonine. There are nine possible single nucleotide changes that can occur in this codon (each of the three possible changes at each of the three positions). Three of these nine changes give rise to another codon that codes for threonine (any change at the third position). We will consider these to be selectively neutral since they don't change the encoded protein, where the large majority of natural selection acts (codon bias is a wrinkle on this rule that I won't cover here). The other 6 changes alter the encoded amino acid to isoleucine, asparagine, serine, proline, or alanine, depending on the specific mutation. In accord with the neutral model of evolution, we will consider as default that all of these changes are selectively neutral or deleterious.


      Summary of what each theory claims:

      Cosmic evolution

      Cosmic evolution is the theory of the origin of the cosmos. The current theory, the so-called Big Bang theory, posits that a large quantity of nothing (yes, nothing at all) decided to pack tightly together (that is nothing packing tightly, not something) and then the nothingness got really hot, and then somehow exploded, and then somehow– and this is key — nothing become hydrogen and helium. This gas is said to have flowed outward through “frictionless space,” which is somehow undertsood to be distinct and separate from “nothing,” and it is also “frictionless,” so the outflowing gas can neither stop nor slow down, yet it did — somehow — to eventually form stars, galaxies, planets, moons, and organized systems. Really. That is the scientific theory. I’m not kidding.

      Stellar evolution

      Steller evolution is the theory of the origin of stars. All of the theories of how stars are born are pretty crackpot, but I’ll detail them later. The fact is that, with one notable exception, we have only ever witnessed stars dying. Furthermore, we can see the scant few death shrouds of novas and supernovas in our galaxy . There seems to be a nova or supernova about every 30 years. If the universe were billions of years old, our galaxy should be chock full of them, but I digress.

      The fact is that, in all of recorded history, we have only witnessed the birth of one star. Just one. About 2000 years ago, a single star formed over a town called Bethlehem.

      Chemical evolution

      Chemical evolution is the theory of the origin of heavy elements. It is based upon the notion that stars can fuse elements heavier than helium, which cannot and does not, and never will happen. It has been theorized that fusion beyond the nuclear 4 gap can occur in the super dense heat of a supernova. It has also been theorized that if a star exploded twice it could fuse past the nuclear 8 gap. Of course, stars never explode more than once but it looks good on paper.

      Abiogenesis—Life from non-life

      Abiogenesis is the theory of the origin of life from non-life, also formerly called spontaneous generation. This is the opposite of biogenesis, the observed scientific fact that all living things were brought forth from a living parent or parents. This is the notion that rocks and chemicals can become living organisms, just add time, chance, and possibly water, as if rocks and raw chemicals were merely plant seeds and seamonkey eggs.

      Abiogenesis is a retread of the centuries old ignorance that stated “If I leave my flour out, it spontaneously generates mice and if I leave my stew out, it generates flies!” Louis Pastuer proved all of this wrong before anyone reading this was even born.

      Macro-evolution

      Macro evolution is the explanation of how slime dreams of a better life, and wishes really hard, and transforms into a fish. Or a fish decides it doesn’t like the water environment for which it was perfectly designed and takes a stroll on the beach despite the fact that it would suffocate. Once it magically transforms from a fish to a salamander, it gives birth to baby lizards. Or lizards lay bird eggs, or a banana tree grows some pears, or an ape transforms into a human being. Or a host of other utterly bogus things along these lines which simply won’t, don’t, can’t, have never, and will never happen. Blind zealous faith in the myth of macro-evolution is foundational doctrine for Darwinists.

      There is such a thing as a “genetic barrier” that cannot be bridged. One species simply cannot transmute or transmogrophy or transform or “suddenly mutate” or even “gradually change over millions and billions of years” into an entirely different species. One kind cannot bring forth offspring of another kind. In addition to just being plain common sense, Gregor Mendel empirically proved it in the early 1800’s and the fact remains today.

      Micro-evolution, the type of “evolution” that is actually science.

      In plain English, micro-evolution is what happens when, say for example, dogs interbreed and make a different breed of … dog. Or when corn pollinates and makes slightly different corn in the next generation of … corn. Or when human beings have human babies or apes have baby apes. In other words, it isn’t even evolution. It is simply modification, variation, or change within kind.

      This type of “evolution” was co-opted by Darwinists and labeled “evolution” because it actually occurs while the other 5 types of Darwinian evolution, to speak plainly, do not.

      By referring to all of them under the single umbrella term of “evolution,” Darwinists can fallaciously claim that ALL types of “evolution” are factual, that “EVOLUTION IS A PROVEN FACT!” I can certainly agree that gradual changes, variations, and modifications within a single, specific kind occurs over time down through generations. However, no one with any common sense can say that this process proves or evidences any of the other grand claims made by Darwinism.

      Darwinists are great at coopting meanings. Note that the first three types of Darwinian evolution most commonly found in today’s biology textbooks have absolutely nothing to do with biology. What do you think the intent is there and what do you think it means? Notice, also, the emphasis, in biology, on “species” instead of on kinds. This is an important distinction.

      Biblical Definition of KIND

      There are many different species of tomatoes. There are cherry and roma and heirloom and beefsteak to name just a few. Every species is all some KIND of tomato. There are many different species of pears. There are bartlett and d’anjou and comice and bosc and the list goes on. They are all some KIND of pear.

      Genesis 1:11-12 Then God said, “Let the earth bring forth grass, the herb that yields seed, and the fruit tree that yields fruit according to its KIND, whose seed is in itself, on the earth” and it was so. And the earth brought forth grass, the herb that yields seed according to its KIND, and the tree that yields fruit, whose seed is in itself according to its KIND. And God saw that it was good.

      Likewise, in the animal kingdom, there are several different species of birds. There are giant ostrich and tiny hummingbird. There are delicious goose and less tasty crow. There are hundreds or thousands of subspecies of birds. They are all different KINDS of bird. There are several different species of fish and every species of fish is some KIND of fish.

      Genesis 1:21-22 So God created great sea creatures and every living thing that moves, with which the waters abounded, according to their KIND, and every winged bird according to its KIND. And God saw that it was good. And God blessed them, saying, “Be fruitful and multiply, and fill the waters in the seas, and let birds multiply on the earth.”

      And there are lots of different species of cow. Lets call them the cow kind. There are several species within the insect kind, the spider kind, the grub kind, and the worm kind.

      Genesis 1:24-25 Then God said, “Let the earth bring forth the living creature according to its KIND: cattle and creeping thing and beast of the earth, each according to its KIND” and it was so. And God made the beast of the earth according to its KIND, cattle according to its KIND, and everything that creeps on the earth according to its KIND. And God saw that it was good.

      Being of a kind simply means that members of that kind can bring forth. “Let [all created things] bring forth…according to [their] KIND.” Darwinists mock the use of the word “kind” because, unlike nearly everything within their idiotic theory, the word kind is accurate. Accurate terms and facts tend to threaten to destroy the foundation of fallacies upon which Darwinists base their religion of secular humanism worshiped through the dogma of methodological naturalism.

      Kinds vs Species

      All dog kinds, for example, can bring forth, uh, more dogs. Now, I grant you that a poodle and a great dane might have some mechanical issues to overcome should they attempt to bring forth, but they are genetically compatible members of dog kind. Likewise a shetland pony and a clydesdale would face some geometric challenges, but they are still two species within the kind of animal we call horse and they can bring forth.

      In terms of kinds, there is a very real genetic barrier that prevents species of different kinds from bringing forth with species of different kinds. My point being, this barrier is far more than a simple mechanical problem. There is a very real genetic barrier that prevents species within a certain kind from magically producing some completely different kind.

      For example, Darwinists are sure to trumpet how “many genetic similarities” exist between the DNA of humans and apes. This is actually incorrect in terms of codons. By way of analogy, the collected works of Edgar Allan Poe and the latest New York City telephone book share 100% of alphabet letters, punctuation, and arabic numbers in common. They do not, however, share even one complete sentence in common. They are not the same kind. Likewise, genetically, human beings and apes genetically are not the same kind. Nor has it ever been shown that humans and apes can or could ever bring forth.

      Darwinists have a real burden of proof. They have to convince people that all life sprang forth from a rock. Then that first “simple” single celled organism somehow decided that having two independent sexes provided a clear evolutionary advantage over asexual reproduction, and “evolved” into every living thing that now lives or has ever lived. In order to get there, you have to believe that the very real genetic barriers that separate and make distinct every living thing within each KIND does not exist.

      Bad news for Darwinists. Those very real genetic barriers exist.

      So how to Darwinists convince you? By shifting the burden of proof. They proclaim, “Because Darwinism is true, one kind simply must have been able to produce a different kind at some point in the past. Prove me wrong.” This can keep people busy for years and removes the burden of proof from them to provide evidence that their fairy tale resembles reality.

      To be conned into believing Darwinian theory, one must be very, VERY gullible. One must accept that dirt and rocks and water can assemble into a living organism, complete with perfectly aligned protiens made of thousands upon thousands of amino-acids arranged in perfect order, chromosomes and ribosomes ready to service them, messenger DNA, RNA, and DNA packed with enough information (all of it absolutely accurate) to fill the New York City Library thousands of times over, perfectly tuned fully assembled interdependent life saving processes , sensory functions, and instinctive instruction sets. One must accept that this can take place in less than a scant few million years and all by unguided and completely random processes. One must accept that this single celled organism then crossed every known genetic barrier to become the “common ancestor” of every living thing and every thing that ever lived on the earth. One must accept all of this without question and blindly ignore any doubts that things like unpleasent facts and the laws of nature might introduce.

      So-called “micro-evolution,” or what is more accurately called changes within kind, unquestionably has taken place since the Cambrian Explosion, continues to take place today, and will undoubtedly take place in the future. But there is not one shred of evidence that any other type of “Darwinian evolution,” has ever taken place, takes place today, or could possibly take place in the future. That includes macro-evolution where one kind of living thing can bring forth a completely different kind of living thing.

      When people used to tell stories about how frogs turned into a prince, our common sense told us we were hearing a fairy tale. Today, people with letters like BA and MS and PH and D after their names believe these same kind of fairy tales. The difference is that a magic wand has been replaced by the magic of time and chance. And based on that magic, some of those otherwise very intelligent people even worship at the alter of those same fairy tales.


      The Limits of Life: Biology and the Philosophy of Nature

      What are the limits within which life can exist? What are the limits of the natural sciences in explaining life and its origins?

      I recently attended a fascinating lecture at Oxford on the existence of a variety of micro-organisms in what would seem an improbable environment: the Atacama Desert in northern Chile, which is the driest place on earth. Professor Rafael Vicuña, a distinguished biologist at the Catholic University of Chile, presented results of his research, which show how life has found ingenious ways to adapt to extreme conditions such as very low water availability, high salt concentration, and intense ultraviolet radiation.

      This research is especially intriguing because the Atacama is seen as a terrestrial analogue for Mars. In fact, NASA is interested in the ways in which research in this desert might contribute to its astrobiology program. For some time, astrobiologists have been studying what are called extremophiles, organisms that live in extreme conditions. Do we get closer to understanding the origin of life the more we advance in our knowledge of life at its frontiers?

      It is precisely such a question that is properly in the domain of the philosophy of nature. It would be of considerable benefit for biologists and other natural scientists to become acquainted with the insights this discipline offers. The philosophy of nature is a more general science of nature than any of the diverse empirical sciences. It depends upon the various natural sciences to understand nature, but the philosophy of nature concerns topics that are not specific to any one of the sciences, but common to them all: the nature of change and time, how physical entities are unities (as distinct from mere heaps of elements), and what the differences are between the living and the non-living.

      What can the philosophy of nature tell us about investigating the origin of life? First of all, it can help us to avoid the errors in various philosophical claims about life and its origins. Questions concerning the nature of living things precisely as living have currency, in part, because of the persistence in modern culture of various materialist, mechanist, and reductionist accounts of living and non-living entities that eliminate any real, qualitative distinction between the living and the non-living.

      There are many who, by accepting a form of materialism and reductionism—that is, by insisting that living things are nothing more than the sum of their physical components—conclude that a question such as “What is life?” is, at the very least, not a biological question, and probably is best rejected as a question without content. So we hear that one ought to resist using the term “life” to describe what is just a highly sophisticated movement of matter. In an important sense, according to such a view, “life,” as something other than matter in motion, does not exist. Life, however, is more resilient than attempts to eliminate it as a category of scientific discourse, not to mention as a feature of nature!

      In The Atheist’s Guide to Reality: Enjoying Life without Illusions, Alex Rosenberg, professor of philosophy at Duke University, tells us that the combination of contemporary physics and evolutionary biology offers an exhaustive account of everything that exists. He enthusiastically embraces “scientism” and its nihilistic consequences. Rosenberg claims:

      If we accept evolution as the mechanism that gave rise to us, we understand that we are nothing more than a highly ordered collection of bio-molecules. Molecular biology has made fantastic strides over the last fifty years, and its goal is to explain all the peculiarities and details of life in terms of molecular interactions. A central tenet of molecular biology is that that is all there is.

      For those scientists and philosophers who embrace some form of materialism, there is a strict disjunction: either we explain the living in terms of material, mechanically operating constituents, or in terms of some mysterious spiritual substance, some vital force. There is no substitute for materialism but magic, for there is no philosophical position other than materialism that is compatible with the science of biology. This is true, so the argument goes, because this mysterious substance, this vital force, yields itself even in theory to no method of investigation. Thus, it must be cast aside, leaving one with the inevitable conclusion that there is nothing more to living beings than their material parts.

      The philosophical analysis that concludes that we must choose between materialism and vitalism, however, is based on a limited understanding of the options. Biologists and other natural scientists ought to avoid a philosophical interpretation of nature that reduces reality to the purely material and empirically observable.

      In this essay, I will leave aside the contributions of the philosophy of nature to what life is, although they disclose compelling alternatives to materialism and vitalism. Rather, I want to focus on change. Surely, change from the non-living to the living—a transition that would have to occur at the origin of life itself—must be instantaneous. At first, this might seem a strange claim. For the changes we observe, for example, a change in place or an increase in some quantity or the growth of an organism, are stretched out in time. They are, at least in principle, observable, even though in some instances we need highly sophisticated instruments to do the observing.

      But any change from the non-living to the living cannot be stretched out in time. How do we know this—and know it prior to any particular change? Whatever entity we might wish to consider: it is either alive or not. We might not know whether it is alive or not, but we know for sure that it is one or the other. An entity cannot be a little bit alive or a little bit not alive. Thus, the change from non-living to living cannot be an observable transition, since the change occurs all at once, instantaneously. Although we only observe empirically those changes that occur in time, we can know that an instantaneous change has occurred. We can recognize new life. Similarly, the change that is death is instantaneous one is not partially dead any more than one is partially alive.

      The term “instantaneous change” may seem to be an oxymoron—but only to those who have not understood the philosophy of nature, as that philosophy explains change. Our world is full of such changes. Living beings produce other living beings. When a new rabbit comes into existence, for example, all sorts of changes occur in the movement of sperm and ovum, but the change from non-rabbit (neither the sperm nor the ovum is a rabbit) to rabbit is instantaneous. This change is, in principle unobservable, although the result of the change is indeed observable. As our knowledge of mammalian embryology advances, we understand more and more the processes that lead to reproduction, but we ought not to identify these processes with the change that is reproduction.

      There might be any number of intermediate biological entities between a non-rabbit and a rabbit, but when we look at any of these entities, they are whatever they are (and not something else) and they are not “partial rabbits.” A series of processes may very well be occurring that will result in a new member of the species rabbit but if we speak of only the sum of these processes as “reproduction,” we are not speaking with adequate precision, either biological or philosophical. Reproduction is not the sum of those changes prior to it rather, it is a distinct change in its own right, even if prepared for by various other changes. The various physical and chemical changes that prepare the way for reproduction are different in kind from the change that is reproduction.

      Obviously, reproduction must be distinguished from whatever change we identify as the ultimate origin of life. In a sense, of course, this radical origin of life is beyond the grasp of biology, since biology seeks to understand changes among already existing living beings. Any explanation of the origin of life requires a science wider in its scope than biology—and wider, as well, than physics and chemistry.

      There was an especially good example of the confusion about change and the identity of living things in an essay by Larissa MacFarquhar in a September 2011 essay in The New Yorker. She was writing about the work of philosopher Derek Parfit, probably most famous for his work Reasons and Persons, and she relates the following thought experiment and draws conclusions from it.

      Suppose that a scientist were to begin replacing your cells, one by one, with those of Greta Garbo, at the age of thirty. At the beginning of the experiment, the recipient of the cells would clearly be you, and at the end it would clearly be Garbo, but what about in the middle? It seems implausible to suggest that you could draw a line between the two — that any single cell could make all the difference between you and not-you. There is, then, no answer to the question of whether or not the person is you, and yet there is no mystery involved — we know what happened. A self, it seems, is not all or nothing but the sort of thing that there can be more or less of. When, in the process of a zygote’s self-multiplication, does a person start to exist? Or when does a person, descending into dementia or coma, cease to be? There is no simple answer — it is a matter of degrees.

      Here we have an example of an analysis that fails to grasp the unity of a human being and the fact that there are no such things as beings that are partially human or partially non-human. The specific change that brings about a human being—the complete fertilization of a human egg with human sperm—is instantaneous. Being a human being is not a matter of degrees. The potentials that human beings have are variously realized through different processes, but these potentials exist in actual human beings. The philosophy of nature shows us how to avoid contemporary confusion in discourse about so-called potential human beings.

      The philosophy of nature can help biologists recognize that living beings possess a unity, and hence an identity, that is properly their own. However complex their structure and varied their material makeup, they are not simply the arrangement of diverse, contiguous parts. For every living being, there must be a principle or source of its unity, a principle from which its characteristic behavior flows. Without such a principle of unity, it would be no more than a heap of material elements existing next to one another. Biologists identify living things by their behavior and structure. If we wish to distinguish the living from the non-living, we begin by such observations, but reason leads us on to ask about the source of this distinctive behavior, a source that, in some sense, is a common feature of life. The philosophy of nature offers the biologists ways to understand this source, this principle of life.

      The philosophy of nature aids biologists in recognizing the kinds of change that exist in the world as well as the need for some kind of unifying principle that is a source of an organism’s identity. The philosophy of nature helps to identify the limits of biological investigation when it comes to understanding the origin of life from no life whatsoever.

      When biologists look at micro-organisms in the Atacama Desert, or living things anywhere, they can know that explanations that only speak of material components offer, at best, an incomplete understanding of life. Biology continues to offer us new and exciting insights into the world of the living. These insights need to be enhanced, as it were, or, perhaps better, integrated into a wider philosophical perspective. In this endeavor, biology needs the philosophy of nature.

      Related Posts

      “Science” can tell us when life begins, provided that we already know what to look&hellip


      Natural Selection is one of the main concepts found within the theory of evolution. It was discovered by Charles Darwin and Alfred Russel Wallace though Darwin championed the idea in his book On the Origin of Species.

      Natural selection can be defined as the process by which random evolutionary changes are selected for by nature in a consistent, orderly, non-random way.

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      Transcript

      What is Natural Selection?

      Natural selection is one of several key concepts contained within the theory of evolution. To understand exactly what natural selection is and why it’s important, let’s first take a quick look at two other evolutionary concepts: descent with modification, and the overarching idea of common descent.

      Descent with modification is the observable fact that when parents have children, those children often look slightly different than their parents and slightly different than each other.

      Common descent is the idea that all living things on earth are related, they descended from a common ancestor. Through the gradual process of descent with modification over many many generations, a single original species is thought to have given rise to all the life we see we today.

      The common descent of all life on earth is not a directly observable fact. We have no way of going back in time to watch it happen. Instead, common descent is a conclusion based on a massive collection of facts. Facts found independently in the study of fossils, genetics, comparative anatomy, mathematics, biochemistry, and species distribution.

      Because the evidence for common descent is so overwhelming, the concept has been around since ancient times but in the past it was rejected by many philosophers and scientists for one main reason: You can not get order and complexity from random chaos alone.

      The bodies and behaviors of living things are extremely complex and orderly, but descent with modification produces random variation.

      All through history, no one could explain how complex life arose from simple life through random variation, until Charles Darwin discovered natural selection.

      All through history, no one could explain how complex life arose from simple life through random variation, No one until Charles Darwin and his discovery of natural selection.

      Charles Darwin who lived from 1809-1882 was a naturalist – someone who studies nature. At the start of his career he traveled the world by ship, collecting and documenting plants and animals.

      During his travels, Darwin became very interested in the idea of common descent. He noticed that Islands contain species of plants and animals unique to those islands – they can’t be found anyplace else on earth – but they often look and behave surprisingly similar to creatures found on nearby continents.

      Tortoises on the Galapagos Islands can be distinguished from those of Africa, meanwhile, with the exception of size, they’re almost identical to a species found nearby in South America.

      Darwin believed these similarities could be best explained through common descent. Long ago a tortoise from the mainland may have drifted to the islands, possibly on a raft of storm debris, and once arriving, laid her eggs. Random changes caused by descent with modification over thousands of years eventually transformed the island creatures and the mainland creatures so much, that they could no longer be considered the same species.

      This idea made good sense to Darwin except for one thing. The island creatures he found weren’t just randomly different from their mainland cousins, they were specially adapted for island life.

      The Galapagos is a collection of 18 main islands many of which are home to tortoises. The larger islands have lots of grass and vegetation. Tortoises there grow extra heavy and have dome like shells. Some of the smaller islands have very little grass, forcing tortoises to feed on island cactus. The best cactus pads grow on the tops of these plants. Fortunately, tortoises on these islands are equipped with expanded front legs and saddle like shells allowing them to stretch their necks extra long to reach their food.

      It’s almost as if these island creatures have been perfectly sculpted to survive within their unique environments.

      How did this sculpting take place? Random descent with modification alone could never do such a thing.

      Darwin drew upon his knowledge of selective breeding to answer this question. For thousands of years, farmers have been taking wild plants and animals, and through the process of selective breeding, have sculpted the original wild forms into new domestic forms much better suited for human use and consumption.

      The process is slow but simple: If a single plant produces 100 seeds, most will grow to be nearly identical to the parent plant, but a few will be slightly different. Some variations will be undesirable – smaller size, bitter taste, vulnerability to disease and so on. Other variations will be highly valued – thicker sweeter leaves for for example.

      If a farmer only allows the best plants to reproduce and create seeds for the next crop, small positive changes will add up over multiple generations, eventually producing a dramatically superior vegetable.

      You might be surprised to hear that broccoli, cauliflower, kale, brussel sprouts, and cabbage are all just different breeds of a single type of weed commonly found along the shores of the English Channel. The evolution of this original plant into all the varieties we see today, was carefully guided by different farmers around the world, who simply selected for different traits.

      It’s important to note that the farmer doesn’t actually create anything. Random descent with modification creates new traits. The farmer simply chooses which of the new creations are allowed to reproduce, and which are not.

      Darwin proposed that nature itself is also capable of selection. It may not have an intelligent brain like a farmer, but nature is an extremely dangerous place in which to live. There are germs which can kill you, animals that can eat you. You could die of heat exhaustion, you could die of exposure to the cold.

      When parents produce a variety of offspring, nature, simply by being difficult to survive in, decides which of those variations get to live and reproduce, and which do not. Over multiple generations, creatures become more and more fit for survival and reproduction within their specific environments. Darwin called this process: natural selection.

      Since Darwin first put forth his idea in the mid 1800’s, natural selection has been studied and witnessed numerous times in nature and in the science lab. What started as an idea is now officially an observable fact.

      Darwin’s discovery has greatly expanded our understanding of the natural world. It’s led to countless new breakthroughs and it finally allowed scientists to seriously consider the idea of common descent.

      So to sum things up, what exactly is natural selection?

      Natural selection is the process by which random evolutionary changes are selected for by nature in a consistent, orderly, non-random way.

      Through the process of descent with modification, new traits are randomly produced. Nature then carefully decides which of those new traits to keep. Positive changes add up over multiple generations, negative traits are quickly discarded.

      Through this process, nature, even though it does not have a thinking mind, is capable of producing incredibly complex and orderly creations.


      Macro-evolution Vs. Micro-evolution?

      Because the theory of evolution does a lot of damage to a literalist interpretation of the bible, many believers and various conservative Christian denominations are reluctant to accept it as valid, even though among scientists relevant to the field, evolution is a settled matter. It is a scientific fact. (If you’re hung up on the term “theory”, as it pertains to evolution, you should know that when scientists use the word “theory”, it has a different meaning from how the word is used in normal everyday conversation. In scientific usage, the term "theory" is reserved for explanations of phenomena which meet basic requirements about the kinds of empirical observations made, the methods of classification used, and the consistency of the theory in its application among members of the class to which it pertains. See: Scientific theories).

      There is plenty of evidence that supports the theory evolution, such as the very fact that disease-causing micro-organisms evolve to become resistant to antibiotics, or how certain pests evolve to become resistant to certain pesticides. Animal breeders have had centuries of experience selecting certain desirable traits in some domestic animal breeds and having them mate with others to produce off-spring bearing the said desirable traits, or sometimes hybrids that may share traits of both. Creationists (i.e. people who reject evolution on the grounds of their belief that all living organisms were created by a ‘God’) normally concede this point, but dismiss it on the basis that it is “micro” evolution, and then go on to insist that “macro” evolution is what is false. Here is an excerpt from an essay called “Microevolution Doesn’t Prove Macroevolution” from the United Church of God website, expounding upon this kind of objection:

      Studies that find small variations within a species over time, such as in the size of finch beaks or the coloration of moths, are sometimes used to try to prove Darwinian evolution. But such studies are sometimes flawed. And even if valid, they provide no such proof.

      Adaptation within a species is called microevolution. It is the same phenomenon at work when the average height of men and women increased by several inches in the Western world over the course of the 1900s. Better health and nutrition played a large part in producing larger-sized people. In the same way, microevolution is at work when breeders produce varieties ranging from Chihuahuas to Great Danes within the one species Canis familiaris —the domestic dog.

      These examples show, as in the rest of nature, that all species do have a margin of change available within their genetic pool to adapt to conditions. This trait is found in man, who can adapt to freezing weather, as the Eskimos do, or to the broiling sun in the desert, as bedouins have done. But bedouins and Eskimos are still human beings, and if they changed environments again, eventually their offspring would also go through minor changes to better adapt to their new environment.

      What has never been scientifically demonstrated—in spite of many examples of wishful thinking—is macroevolution, or the change from one distinct species to another. Dogs have never evolved into birds or human beings.

      But is this objection a valid one?

      The term "macroevolution" frequently arises within the context of the evolution/creation debate, usually used by creationists alleging a significant difference between the evolutionary changes observed in field and laboratory studies and the larger scale macroevolutionary changes that scientists believe to have taken thousands or millions of years to occur. They may accept that evolutionary change is possible within species ("microevolution"), but deny that one species can evolve into another ("macroevolution"). Contrary to this belief among the anti-evolution movement proponents, evolution of life forms beyond the species level ("macroevolution", i.e. speciation in a specific case) has indeed been observed multiple times under both controlled laboratory conditions and in nature. The claim that macroevolution does not occur, or is impossible, is thus demonstrably false and without support in the scientific community.

      Such claims are rejected by the scientific community on the basis of ample evidence that macroevolution is an active process both presently and in the past. The terms macroevolution and microevolution relate to the same processes operating at different scales, but creationist claims misuse the terms in a vaguely defined way which does not accurately reflect scientific usage, acknowledging well observed evolution as "microevolution" and denying that "macroevolution" takes place. Evolutionary theory (including macroevolutionary change) remains the dominant scientific paradigm for explaining the origins of Earth’s biodiversity. Its occurrence is not disputed within the scientific community. While details of macroevolution are continuously studied by the scientific community, the overall theory behind macroevolution (i.e. common descent) has been overwhelmingly consistent with empirical data. Predictions of empirical data from the theory of common descent have been so consistent that biologists often refer to it as the "fact of evolution".

      From: Macroevolution – Misuse

      So what is macroevolution anyway?

      In science, macro at the beginning of a word just means "big", and micro at the beginning of a word just means "small" (both from the Greek words). For example, "macrofauna" means big animals, observable by the naked eye, while "microfauna" means small animals, which may be observable or may not without a microscope. Something can be "macro" by just being bigger, or there can be a transition that makes it something quite distinct.

      In evolutionary biology today, macroevolution is used to refer to any evolutionary change at or above the level of species. It means at least the splitting of a species into two (speciation, or cladogenesis, from the Greek meaning "the origin of a branch", see Fig. 1) or the change of a species over time into another (anagenetic speciation, not nowadays generally accepted [ note 1 ]). Any changes that occur at higher levels, such as the evolution of new families, phyla or genera, are also therefore macroevolution, but the term is not restricted to those higher levels. It often also means long-term trends or biases in evolution of higher taxonomic levels.

      From: Macroevolution – Its Definition, Philosophy and History

      Is there evidence for macroevolution? Yes. TONS.

      Former Professor for Public Understanding of Science at Oxford University, Richard Dawkins, makes a case for evolution in this interview:

      In the video below, biologist Kenneth Miller talks about the relationship between Homo sapiens and the other primates. He discusses a recent finding of the Human Genome Project which identifies the exact point of fusion of two primate chromosomes that resulted in human chromosome #2:

      To view Kenneth R. Miller’s full lecture (1hr 58min 42sec): The Collapse of Intelligent Design, go here. (Kenneth Miller is a Roman Catholic, by the way)

      A Nice Analogy:

      I came across a rather simple and interesting way of explaining to creationists HOW macroevolution works. I don’t know who first came up with it, but I found it via Reddit. It goes like this:


      Watch the video: Τι είναι η Ειδική Θεωρία της Σχετικότητας; (October 2022).