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How to calculate the effective population size ($N_e$) with overlapping generations?
From this Source: If generations are overlapping, then the effective population size $N_e$ does not equal the population size $N$.
I know mathematical formulations in order to find the effective population size $N_e$ when the sex-ratio is biased $\left(N_e = \frac{4N_mN_f}{N_m+N_f}\right)$ or when the population size varies cyclically through time $\left(N_e = \frac{n}{\sum_{i=1}^n\frac{1}{N_i}}\right)$.
What is the effective population size in a population with overlapping generations?

Some info on this can be found in Hendrick (2010, p 234), and if you follow the reference there (Laikre et al, 1998) you might find a more complete answer.

Moved misposted answer (see below) from http://biology.stackexchange.com/questions/23098/effects-of-selection-on-effective-population-size

There are many different ways to do this, depending on what assumptions you make on e.g. stable age structure, distribution of offspring, haploidy/diploidy, population growth etc. As you probably know, there are also two main approaches to effective population sizes, namely ones based on:

the rate of inbreeding ($N_{e,i}$)
the increase in variance of allele frequencies ($N_{e,v}$), and they can sometimes differ a bit from each other.

An early attemp to calculate effective population size with overlapping generations comes from Felsenstein (1971), which is based on life table information. This paper includes several derivations, both on inbreeding $N_e$ and variance $N_e$. As an example, the formula of inbreeding $N_e$ for a diploid population is:
$N_e = \frac{N_1T}{1 + \sum_i^\inf l_i s_id_i v_{i+1}^2}$
Here, $T$ is the generation time, $l_i$ is the survival up to age i, $s_i$ is the survival from age i, $d_i$ is the probability of death at the end of age i (i.e. 1-$s_i$), and $v_{i+1}$ is the reproductive value of individuals in stage i+1. However, this is assuming a constant population size and a stable age distribution, so some rather restrictive assumptions. The paper also includes models for haploid populations, and I haven't gone through it carefully.
A couple of more recent papers that calculate $N_e$ for overlapping generations are Engen et al. (2005) and Engen et al. (2007). These papers use diffusion approximations to derive several formulas for $N_e$ under different assumptions, for age-structured density independent populations. One model for a haploid population in a fluctuating environment is:
$$\begin{align}
N_e & = \frac{N}{\sigma_d^2T} \text{, with} \\
\sigma_d^2 & \approx \sum_{i=0}^k \frac{\lambda^{-2}}{u_i} \left[\left(\frac{\delta\lambda}{\delta b_i}\right)^2\sigma_i^2 + \left(\frac{\delta\lambda}{\delta s_i}\right)^2 s_i(1-s_i) + 2\left(\frac{\delta\lambda}{\delta b_i}\right)\left(\frac{\delta\lambda}{\delta s_i}\right) c_i \right]
\end{align}$$
where $T$ is the generation time and $\sigma_d^2$ is the demographic variance. In the lower equation, $\lambda$ is the deterministic growth rate (dominant eigenvalue), $u_i$ is the proportion of the population in stage i (a component of the stable age distribution), $b_i$ is the expected number of offspring with variance $\sigma_i^2$, $s_i$ is the survival rate of stage i (with binomial variance), and $c_i$ is the covariance between reproduction and survival. You really need to dive into these papers to fully understand how these equations are derived and how the variables are defined (will take up far to much space here). However, at its core, it is using a diffusion approximation on an allele at a selectively neutral locus.
There are many other papers out there deriving effective population sizes, under different assumptions and restrictions, but the ones cited above should be a good starting point, and by looking at citations to and from these you should get a decent overview of the topic. Many textbooks also cover ways to calculate effective population sizes with overlapping generations, e.g. Hedrick (2011) and Felsenstein (2013, free pdf).

Ha ha I actually didn't even notice either that it was under the wrong post!

@Remi.b Yeah, I didn't at first either. Also, upvote if you find it useful, so that the Q is removed from the unanswered queue.


text:
Influence of temperature on protein binding and decay rates
For computer modeling purposes, I am looking for some referenced quantitative measurements of the effect(s) of temperature on the dynamic of biochemical reactions.
Question
In particular, my question is:
How does temperature influences rate of protein binding to a DNA-binding motif?

I am NOT looking for..
I am not looking for a theoretical explanation of how temperature influences this process (I have basic understanding of the importance of the activation energy as displayed on a Maxwell-Boltzman distribution and of the michaelis-menten equation).
I am looking for..
I am looking for a function (eventually expressing the michaelis-menten constant as a function of the temperature) that I can plug in my algorithms to incorporate the influence of various temperatures on the process I want to simulate. I want to simulate the average gene network from an average standard eukaryote. For other purpose, I chose parameter values coming from the yeast. A index such as the $Q_{10}$ would be very useful as well.
Similar already-answered question
A similar question (influence of temperature on transcription rate) has been asked and answered (by @rhill45) here.

Let's do your second one first, you want to find the rate of binding:
Yes you are right you need to calculate for km
I think I found the paper that ties temp into the reaction rate:
statistical approach called response surface methodology (RSM) is used for the prediction of the kinetic constants of glucose oxidase (GOx) as a function of reaction temperature and pH. Lineweaver–Burk transformation of the Michaelis–Menten equation was utilized as the integral part of the RSM algorithm. The effects of variables, namely reciprocal of substrate concentration (0.033–0.5 mM−1), reaction temperature (14.9–40.1 °C) and reaction pH (pH 4.4–8.5) on the reciprocal of initial reaction rate were evaluated and a second order polynomial model was fitted by a central composite circumscribed design (CCCD).
Biochemical Engineering Journal
August 2005, Vol.25(1):55–62, doi:10.1016/j.bej.2005.04.001
A new approach for determination of enzyme kinetic constants using response surface methodology
İsmail Hakkı Boyacı
For the decay
I'm still working on the first I'm suggesting to check this out but need sleep I can read it tomorrow
http://www.eastcoastbio.com/productcart/pc/stability_testing.asp

Nice. Your article answers my second question (although the article is about enzymatic reaction and not specifically about protein binding). I made the mistake to ask several questions in one post. I restricted this post to one question and checked your answer. Here is the other question. Thanks a lot

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text:
How does temperature influence the rate of protein degradation?
For computer modeling purposes, I am looking for some referenced quantitative measurements of the effect(s) of temperature on biochemical reactions.
Question
In particular, my question is:
How does temperature influence the rate of protein degradation?
Reducing the scope of my question
My question might be too broad in the sense that there might have too much variance in how different proteins respond to temperature in order to draw in general tendency. If this is so, someone willing to answer may reduce the question to transcription factor, to General Transcription Factor (GTF) or even to TFIIA.

I am NOT looking for..
I am not looking for a theoretical explanation of how temperature influences this process (I have basic understanding of the importance of the activation energy as displayed on a Maxwell-Boltzman distribution and of the Michaelis-Menten equation).
I am looking for..
I am looking for a function that I can plug in my algorithms to incorporate the influence of various temperatures on the process I want to simulate. I want to simulate the average gene network from an average standard eukaryote. For other purpose, I chose parameter values coming from the yeast. A index such as the Q10 would be very useful as well.
Similar already-answered question
Similar questions have been asked and answered here and here

Temperature dependence of rate parameters can be obtained by Arrhenius Equation. $ \frac{k^∗_1}{k_1}=e^{ \left(-\frac{Ea}{R}\Big[\frac{1}{T^∗}−\frac{1}{T}\Big]\right)}$

Catalysed reaction also depend on the structural stability of the catalyst (for e.g. enzymes). Also it is important to know if a reaction is primarily enthalpy/entropy driven. These are more of general comments about dependence of any reaction on temperature.

Yes, I looked at a bunch of equation of the kind (Eyring and Van't Hoff) but it didn't really help me to infer any values for protein decay.

Are you looking at protein decay in eukaryotes? If so then you can see the effect of temperature of proteasome structure. Nonetheless, the rates of degradation for different proteins would be different. Can you state your precise problem because this is kind of very broad.

Thanks for your comments @WYSIWYG. I just need some general effect of temperature on protein decay. The proteins I will models are transcription factors. I am not interested in any single protein but I would like to have some appreciation, an order of magnitude of how globally speaking the temperature influences the rate of protein decay. I need that for simulation purpose, that is why I am not restricted to any protein but would need to know the influence of temperature on the average protein. Please let me know if my question is still too broad or too vague.

I want to simulate the evolution of genetic architecture when after a sudden change in temperature or in an environment that is temporally heterogenous in terms of the temperature.

It is broad in the sense that not all enzymes will be equally susceptible to temperature. You can assume that they are at their optimum. Perhaps you can model the loss of activity due to increased temperature (protein denaturation) as gamma/chi square or exponential function and the effect of low temperature using the chemical thermodynamic equations. But the point is that we don't know if they are at their optimum or not or how each of they may perform at different temperatures.

This comment applies to all the three related questions. Degradation might be the simplest because all you need to worry is about the activity of proteasome (the very basic degradation process not considering different ubiquitin ligases etc). In bacteria you may consider some 'x' protease instead of proteasome and assume saturation kinetics for degradation.

Let us continue this discussion in chat.

This is a general answer for all three of your related questions:

This one
How does Temperature influences the rate of protein turnover?
How is the rate of transcription influenced by temperature?

Since you said:

I want to simulate the evolution of genetic architecture when after a
  sudden change in temperature or in an environment that is temporally
  heterogeneous in terms of the temperature

You should see this paper. They have studied the dependence of growth rate on temperature. This was measured for different organisms (all poikilotherms).
See this figure:

Figure S1 from Dell et. al 2011. PNAS

The unimodal thermal response of radial growth rate of sac fungi (m/(colony*s)). Green and brown curves are OLS regressions to the Boltzmann-Arrhenius model (Eq. 1) for the subset of data that are the rise and fall components, respectively. These components were extracted by the algorithm described in the Materials and Methods. For
  this particular response, the rise was obtained by the algorithm
  through removal of the measurements at the 4 highest temperatures and
  the fall through removal of measurements at the 5 lowest temperatures.
  The blue curve is the best fit to the Johnson & Lewin model (SI
  Methods) (1). Values shown are estimated activation energies with 95%
  confidence intervals for the respective response components. Dotted
  vertical arrows are estimated temperatures for Topt –the temperature
  at which the trait value is optimal–calculated from the direct method
  (red) and Johnson & Lewin model (blue). See SI for more details. Data
  are from Fargues et al. (9)

As you can see, the curve is skewed. The left side of the curve from Topt follows the activation energy model (Arrhenius/Boltzmann). The right side, though they say in the figure legend also fits the model, it seems to me that the phenomenon is somewhat different. The steepness of the curve is perhaps because of denaturation of enzyme active site and therefore a loss of activity. For different enzymes the denaturation kinetics would vary.
I happened to stumble into the author of this paper and that is how I got to know of this work. He seemed to agree that the right half steepness might be because of enzyme denaturation.
Also have a look at this article which is about the temperature dependence of the activity of the proteasome of a thermophile (same principles apply for mesophiles).
Figure 2 of the article:

Normalized Cbz-Ala-Ala-Leu-pNA hydrolysis activity of (filled squares)
  purified native and (filled circles) 70°C heat-purified recombinant (α
  + β) Mj proteasome. To normalize recombinant activity to native activity a multiplier of 1.92 was used. Error bars for the native
  sample represent one standard deviation for triplicate assays.
  Measured temperature errors were ±2°C.

Bottomline (From my comments)
You can assume that the enzymes are at their optimum. You can model the loss of activity due to increased temperature (protein denaturation) as gamma/chi square or exponential function (or the fitted function on the denaturation kinetics) and the effect of low temperature using the chemical thermodynamic equations. But the point is that we don't know if they are at their optimum or not or how each of they may perform at different temperatures.

Nice! I very much appreciate your answer and how much you designed it to apply to my specific needs. Thank you!

Yes, the loss on the right side is because of too high temperatures. Actually the proteins denaturate by too low temperatures or too high pressures as well.

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Are there animals which produce their own colour pigments`?
After discovering flamingos natural colour isn't red, pinkish as one naively may have assumed though, I looked into the matter more deeply. I also found out that many other animals do not produce the respective colour pigments matching then their outer appearance.
Some seem to use certain optical effects like Rayleigh Scattering or applying several different layers of filters to absorb wave lengths of light from the spectrum just emitting the intended colour.
After browsing through some more cases several questions arose, which of course cannot be answer all in detail here. However most interesting to me were:

Why does it seem so difficult for animals in contrast to plants to produce their own colour pigments?
Some colours seem to be more difficult to achieve than others. For example I found that melanin and several variation of it as colour pigments are produced quite commonly by animals, however blue rather difficult. As the following post indicates, blue colour pigments seem to be in general difficult to produce: Why are so few foods blue?

As my previous comment on the question suggesting improvements has been read I have removed it. I'd suggest you clear your own when you have made modifications. That way just the science will be left, and, as I have said, I think it is worth discussing. If you don‘t get round to editing it yourself, I‘ll do it. Editing of questions and answers is encouraged where it will improve them.

@David, I edited parts but you are always welcome to edit / expand the question further. I consider myself not very well versed, maybe naive in the topic and thus miss certain opportunities. Better there are more questions / thoughts put together encouraging thinking and constructive discussions than ideas missed and lost.

Fine. If I ever get time I'll look in more detail into the metabolic pathways involved (although I'm not sure that this is your field.) As far as the patch of blue is concerned, I imagine that we are getting into colour chemistry as well as metabolism. The story of indigo comes to mind. However the title of the other post puts me off. Is it concerned with edibility or abundance? But I have given that question a wide berth.

Although no expert in this field, I think this question is interesting and I would like to put forward some tentative answers.
In a general evolutionary sense one might regard coloration as a secondary feature of living things — especially animals. Their primary concern is to obtain energy, synthesize structural components, reproduce etc. So the metabolism that evolves under this pressure is such as to allow them to do these things. Colour is not a primary concern. Pigments are complex molecules, and enzymes are only likely to evolve to produce them if there are already reasonably complex precursors available. I know little about plant biochemistry, and would welcome improvement of this answer, but it would seem to me that (1) plants have more complex or different metabolic pathways than animals, such as those for synthesizing chlorophyl, and (2) the pathways for producing pigment precursors are more widespread, perhaps because coloration affords a greater advantage to plants — for pollination etc.
There does appear to be pigment synthesis in animals — an article on pigmentation in bird feathers can be found on the website of The Cornell Lab, for example. That states that the pigments in bird feathers are based on three types of compounds: carotenoids (red or yellow), melanins (black to reddish brown and pale yellow) and porphyrins (pinks, browns, reds and greens).
Two of these are related to compounds the birds synthesize themselves from amino acids — melanins for skin pigmentation, and porphyrins for haem. However, it appears that carotenoids are not synthesized by the birds that employ them for coloured feathers, but are acquired from plants in their diet.
Finally, one might argue that producing colours by optical effects has certain advantages over the use of pigments, namely that they are ‘tunable’ — can be modulated or turned on and off.

I like this answer, but it seems to me that colouration need not always be a secondary concern. Colouration has evolved, for example, as camouflage or for mimicry.

I agree that it is important for those animals that use it. And of course they don't care how they achieve it — pigmentation or optics. My thought was just that central metabolism came first — indeed the terminology "secondary metabolism" reflects this. The pathways leading to anthrocyanin production seem to be widespread in plants, and quick googling indicates that anthrocyanins are made via the phenylpropanoid pathway, which apparently is used for other important non-pigment plant functions. Hence it would seem a more general plant feature. (Where are the plant biochemists when you…?)

Ah, you're thought is well taken.


text:
Zinc and brewer's yeast
I'm hoping for some information relating to yeast nutrition (specifically Saccharomyces cerevisea) in beer fermentation.
Zinc is well-known to be necessary for yeast cultures to perform successful fermentations and since fermentable sugars derived from barley are often deficient in zinc, they are sometimes fortified with additions of zinc sulfate or zinc chloride. This guy tried zinc gluconate, a readily available dietary supplement for the same purpose, and found it to have very little, if any, positive effect on fermentation performance. As he even suggests at the end of his paper, could this be because yeast can't assimilate Zn from this molecule the same way it does from the other two?
Is this question better directed towards Chemistry SE?
Thanks in advance for any help I can get on this.

You can ask this question in homebrew.stackexchange.com

Thanks, I actually came here from the HBSE to ask this question.


text:
How can action potentials be generated through thoughts?
I think I understand how a signal is transferred from neuron to neuron (from  How do the brain and nerves create electrical pulses?).
My question, however, is not about the standard textbook material on neuronal transduction. Instead, I am wondering how an electrical impulse starts in the first place in the brain. In other words, how can an action potential be generated de novo from a conscious thought? I am wondering primarily about a signal like a movement of an appendage or something similar.
So in short, my question is:
How is the 'first' action potential generated in the cortex when one consciously/voluntarily decides of moving a limb (or comparable action)?

You can have a look at this article. It is not accessible to me right now. When I get to read it then I'll post an answer.

@WYSIWYG interesting article! Waiting for your answer!

Where are my comments? Is this your doing @CRags lol?

@ChrisStronks not mine. Some moderator cleaned up!

This paper addresses the question that you are asking. The study is based on conscious decision making in rats.
Performance of rats were assessed in the following task:
The rats have to poke their snout into a waiting port until a auditory signal (beep) is heard.
After first beep the rats can get a small reward (small amount of water) in the reward port. However if the rat waits for a second beep it gets to have a bigger reward.
So there were three behavioral outcomes in the experiment done with 37 individuals:

Short-poke trials: Those that didn't wait for the signal (8.3 ± 4.6%)
Impatient trials: Those that waited for the first beep but did not wait for the second beep (58.8 ± 3.9%)
Patient trials: Those who successfully got the bigger reward. (32.9 ± 1.4%)

The impatient ones did not usually quit immediately after the first beep. This was not a lethargic response to signal but a moment of conflict between two decisions. There was a distribution of waiting times. The patient ones immediately responded to the second beep and took their reward.
Neural activity was studied in the M2 (secondary motor) cortex of impatient rats during the tasks and two kinds of neural activity was observed:

Ramp-to-threshold: Firing rate kept increasing till the end of waiting time suggestive of a neural threshold to quit waiting. (Fig 1)
Steady-transient activity: Firing rate correlated with waiting time i.e. higher firing rate higher the waiting time. This could constitute the neurons that are making the rat wait. (Fig 2)

Figure 1: Ramp-to-Threshold.  Reproduced from Murakami et al. (2014) Nature Neuroscience 17,1574–1582

Figure 2: Transient activity.  Reproduced from Murakami et al. (2014) Nature Neuroscience 17,1574–1582

It was also observed that this activity is task specific. The neurons that showed a certain activity in the above tasks did not behave the same way in the same rats performing a slightly different task.
So action potential is generated in the brain by "thoughts". However these thoughts are not generated de-novo. Any conscious decision is based on memory of past events and similar events evoke those memories, which in cellular terms is generation of action potential. In this case the action potential is generated in response to the rats' sensory input in the form of a beep. Other kinds of responses are instinctive and the brain circuitry has evolved to generate these kinds of responses to certain stimuli such as fear. The above example is a study of conscious decision making based on an acquired memory.

Does neurons that fire on their own (like the reticular activating system) have any role in thought generation?

@CRags Thoughts are essentially memories that are developed out of conscious actions and perceptions. The neurons that "fire on their own" are either controlled by an inbuilt oscillator (look at FHN model) or regulated by visceral stimuli. Then there are reflexes and other instinct driven responses which are basically preformed circuits. I am not sure how the autonomous nervous processes can affect conscious response- perhaps in an indirect way but I guess they don't have a direct role.

What about say, you come across an interesting formula for the first time and you start to think on how that formula was derived. In this case, would the short term memory be responsible for the string of thoughts that follow?

Very interesting answer though. +1 for that

@CRags I think when you are thinking about the derivation of that formula then you are essentially recalling similar formulae and trying to work it out in a similar fashion.

Wow, this is really cool! What a great article! Thanks for that awesome answer! So is the order/rate in which those memories are recalled totally random or is there some sort of mechanism behind what memory is 'remembered'? For instance, if an organism has two memories of an event that correlates with a situation it is currently in, what controls which memory 'shows up'? This is probably a stupid question and the answer is probably right in front of my nose, but still...

@CDB I can't say anything for sure but the strongest memory should be recalled first even though the temporal order of the memory registry might be different. How temporal information is stored in the brain is still under research.

There's a philosophical issue here: if you believe "thought" originates outside of the physical brain, you are probably some sort of dualist. However, I believe it is more likely that the brain is the machine that enables (apparent) consciousness and thoughts. If you believe "thought" causes neurons to fire, you might not be able to find a scientific answer, unless you can measure that "thought" external to the brain...
There are spontaneous activity in the brain. That is, neurons will fire by themselves with or without any external input at random times. See this question and my answer there in. Brain is surprisingly noisy. Perception and behavioral variability is often correlated with such noise, and studied in neuroscience. For example, see

Nienborg, H. and Cumming, B. G. (2009). Decision-related activity in sensory neurons reflects more than a neuron’s causal effect. Nature, 459(7243):89-92.

While I'm not a dualist myself, how do you address causal mechanics originating thought when quantum physics has ruled out local determinism? Global determinism could still exist technically, but that would be an infinitely more complicated model of neuroscience.


text:
A better definition of ‘evolution’?
Occasionally on this site I’ve noticed the following definition of evolution being given:

Evolution is a change of allele frequency through time in a population

( For example, from here)
Recently the following (From here):

The statement "evolution is driven by mutations" is very misleading if not simply wrong. Just have a look at an intro course to evolutionary biology!
In short, evolution is a change of allele frequency over time.

But mutations are important to evolution, and I feel must be included in the definition.
Here’s why that concerns me:  One group of evolution deniers, and others like them, use comments like these above to try and show a weakness in evolution.
See:
http://creation.com/don-t-fall-for-the-bait-and-switch
With comments like:

“It is a bit of a trick played by using sloppy language. Evolutionists use adaptation, which is observed, to support evolution, which is an entirely different process. It is an example of bait and switch.”

Also:

“Next time someone says that evolution is an observed scientific fact make sure you get them to clearly define what they are talking about.
  They will almost certainly be referring to adaptation but want you to believe they have proved evolution.
  Don’t be fooled. Sloppy language leads to sloppy thinking.”

Ok, I hate to give them ‘air-time’ by posting that, but perhaps it will cause us to clarify ourselves better.  Answers like "a change of allele frequency through time" just end up feeding right into this kind of evolution-denial.
SO, here’s my question  Since there’s a lot people here smarter than me:  Can someone please offer up a stronger definition of evolution than just ‘a change in allele frequency over time’?
In researching answers I found this on Berkeley’s site:

“Biological evolution, simply put, is descent with modification. This definition encompasses small-scale evolution (changes in gene frequency in a population from one generation to the next) and large-scale evolution (the descent of different species from a common ancestor over many generations).”

I like that it goes beyond small-scale (which these others would just call adaptation).  But this doesn't mention natural selection, and mutations; which I feel are important distinctions given the kind of comments these deniers make. Is there a reason Berkeley's definition didn't include those distinctions?

EDIT:  Can someone explain the down-vote?  I followed everything outlined under How to ask a good question

You might try over at T.O. ... http://www.talkorigins.org/faqs/evolution-definition.html

I agree with the Berkeley statement and your assertation. "Change in allele frequency over time", by itself, is not sufficient.

But they're the ones using sloppy language.  Evolution IS adaptation which takes place over generations.  The mechanism by which the adaptation happens is mostly change in allele frequency, though other things like mutations, viruses inserting bits of genetic material, or wholesale co-opting of different life forms (e.g. mitochondria) play their parts too.  But we could observe the fact of evolution without knowing anything about the underlying genetic mechanisms.  As Darwin & Wallace actually did.

@jamesqf:  As I read it (and I have read a lot of their material), creationists fully believe in adaptation, speciation, and natural selection.  In their minds, these mechanisms are built into life’s design.  Animals where created with plenty of built-in variety, seen as adaptation via natural selection.  As rabbit spread the globe, some became desert rabbits, some arctic rabbits.  All done via a ‘change in (PRE-EXISTING) allele frequency over time’.  So when we use that phrase as our definition of evolution, it just confirms to them their own stance.

(cont).  The important ‘distinction’ I’m looking for: is one that includes the creation of new genetic information, which as I understand it to be, is via mutation.

It is pointless to say that "Creationists believe adaption" without knowing what they mean by it.  It is not at all clear that they are using the term to mean hereditary changes to the DNA. Also, not being tethered by the evidence, some Creationists might mean something very very different from other Creationists.

And the issue is, of course there is no evidence of all this "pre-existing" variation.  When a bacterial sample evolves resistance, it's due to a DNA change. There was no resistance allele hiding, it didn't exist until the mutation.  Creationists are just wrong about the facts here.

@RunzWitScissors: Well, the operative phrase there is "creationists fully believe".  It's a religious belief, and no amount of fact or logic is going to change that.  The most you can get them to do is weasel around the idea, with their "adaptation".

"Change in allele frequency" also encompasses mutations. The occurrence of a new mutation increases the frequency of that variant from 0 to whatever percent of the population the organism that carries it comprises. Similarly, the frequency of all the other variants at that locus decreases by the same percentage.

@RunzWitScissors the problem is the continued generation of new genetic material is not actually necessary for evolution, so there is absolutely no reason to add it in to the definition. If starting right now no mutations at all occurred ever, life would still continue to evolve for quite a long time.

@swbarnes:  True, but that doesn't see to deter them.  BTW, your bacterial resistance example quite frankly is one example of what they would call a bait-and-switch.  See here. and here.

"Evolutionists"... I'm not sure that I like that term. How about "members of the general public that understand reason". But more seriously, this should go on meta, not the main site. It seems to be more about if we, as a community, should be more careful when defining evolution.

The focus upon definition of term is a rhetorical strategy often employed to confuse the issue, especially by laity when discussing technical matters. Word meaning and use are easily a source of ambiguity, confusion, contradiction and such that the appeal to the dictionary definition is often an appeal to false authority. At the very least it is worth pointing out that aside from etymology and morphology, empirical disputes are not settled by consulting Merriam-Webster.
"Evolution" is a fairly general concept and it can be useful when discussing technicalities to specify "biological evolution".  For example, "water" might mean specifically "H2O" or generally "clear, odorless, tasteless liquid"  or possibly might be used without qualification in distinguishing "potable water" from ocean water, et cetera.
Consider that the definition of term is not so much the pertinent legacy of biological evolution (pace Darwin, Wallace et. alii). The kind of explanation biological evolution uses is it's hallmark. In particular, biological evolution removed teleology (the study of purpose)) from biology.
For example:
A teleological explanation of biology:
1) This plant is photosynthesizing in order to survive.
2) This plants species is surviving.
A biologically evolutionary explanation of species:
1) This plant is photosynthesizing
2) Plant species which photosynthesize have an increased likelihood of survival.
Note that survival is still at play, but the argument is no longer circular. This is not to say that cause has been cited, however, the logical structure of the argument no longer "begs the question"

I think that any definition that is useful for explaining evolution to laymen must contain the concept of natural selection. Here's my version:
Evolution is a gradual change in organisms over many generations caused by the combination of (1) random variation in genes among individuals and (2) the fact that those individuals that best survive and reproduce will transmit their genes to the next generation.${}^1$
That random variation arises (in part) from mutations is just a detail, in my mind. The fundamental mechanism, which is very hard to deny, is that even though variation in any individual's genes are completely random, the combination of this variation with survival of the fittest will produce a strong tendency to adapt to the environment. If someone has difficulty accepting / understanding this principle, it's easy to setup computer simulations that demonstrate the effect hands-on. In my experience this convinces pretty much everyone.
The "bait and switch" argument on the page you link to is fundamentally flawed because there is no difference between what they call "adaptation" and "evolution". Both are evolutionary processes, resulting from variation and natural selection. It's just that these creationists are willing to accept some consequences of evolution, but refuse to accept all of them. This is just a rhetorical device that lets them accuse scientists of "equivocation" (which sounds pretty sophisticated). Also, there are the usual falsehoods: "no intermediate forms", "mutations do not generate new information", and evolution is "just a hypothetical philosophy without observational scientific support".
But then again, creationist do not really care about the scientific arguments --- they only pretend to do so because they know science has credibility. They are driven by belief, and arguing with them is usually pointless.

${}^1$ I am aware that this is not the most general definition one could give, as it does not encompass genetic drift and other mechanisms described by modern evolutionary theory. But I think it's an appropriate one for explaining evolution, as in the discussion brought up by the OP.

I do like that answer.  Thank you.  BTW, please do share at least some of the bigger errors on that page.  An acquaintance gave me that link, and I'd love to point out some of them.

recombination is not necessary for evolution

If you really want to understand evolution consider explaining artificial selection first, it is basically the simplified version. these videos are very good approach laying everything out very clearly. https://www.youtube.com/watch?v=GhHOjC4oxh8.

@John,  I did not mention recombination?

@RunzWitScissors, I have added some comments on the creationist page. If you want to dig deeper, I would recommend Ken Miller's work, http://www.millerandlevine.com/km/evol/

This is a bad answer.  Modern evolutionary theory recognizes natural selection as one of the main drivers of evolution, but by no means is it the only one. Ignoring the past 75 years worth of understanding is a very bad start to a definition.

@Roland.  Good link, will check it out.  Regarding no difference between adaptation and evolution:  one is a subset of the other.  You can have adaptation without any change in existing genetic information.  Finch beak adaptation is one example.  Yes, creationists are willing to accept what you call 'some consequences':  just adaptation.  The entire point of their article is that too often 'adaptation' examples are given as examples of a process that can 'turn molecules into men'.  And the whole point of my post is to create a better definition of evolution than 'change in gene frequency'

@Roland.  Also, can you provide me some links refuting their case that mutations do not generate new information?  I'd love to pass those along.  (I have searched myself some but would like to find better ones).

@RunzWitScissors,  I think "information" is poorly defined in this context. But it's rather obvious that mutations can create new sequences, and so it provides a mechanism for exploring new genotypes.

@iayork,  I wasn't trying to summarize modern evolutionary theory. (And I don't think I'm the right person to do it.) I was shooting for an easy-to-understand definition that conveys the basic idea to the layman. And to be pedagogical, I think variation + natural selection should be the centerpiece.

If you're going to come up with a definition for a layman, maybe it shouldn't be actively misleading.  Your definition here is actively misleading.

@Roland My mistake your wording was a bit confusing.

Remember to define evolution you are looking for the basic observations that the theory was created to explain. That observations is that the variations in a population change over generations. points for mentioning equivocation,

@iayork  I don't think this is "actively misleading". I understand that "change in the heritable characteristics over generations" or some such is a more general definition that encompasses modern evolutionary theory, but that wasn't the issue here. I think it is necessary to focus on natural selection first if you want to teach evolution. If you wanted to teach physics, you would start with Newton's mechanics, not general relativity; Newton's is not the most up-to-date theory, but that doesn't mean that teaching it is misleading. Basics first. Refinement comes later.

If you don't actually understand what evolution is, you shouldn't be answering questions about it.

@Roland focusing on natural selection is counterproductive, a great way to explain evolution is to compare the mechanisms, natural selection, sexual selection, artificial selection, ect. To use your analogy we don't define thermodynamics as only Conservation of Energy.

@John,  well I agree with that. I very much like how Darwin introduces selection in the origin of species by starting with selective breeding, for example.  But those are all selection mechanisms, and I think they are included in the definition I suggested ("individuals that best survive").

except survival is secondary to passing on your genes, that is how things like kin selection and such function. may behaviors drastically reduce survival in return for helping spread genes, you could eliminate survival from the definition entirely. .

This is the one I use for my intro to biology students.
"Biological evolution is a change in the number of times specific heritable characteristics(aka genes) occur within a population over time."
Keep in mind this is a description of the definition{law} of evolution not the theory of evolution. And is just a diffrent way of stating, "Evolution is a change of allele frequency through time in a population"
I use this becasue the possibility of evolution in machines, epigenetics, and xenobiology is discussed and students have a difficult time separating genes and nucleotide sequences, and not every student will know what allele frequency means.
defining evolution with natural selection would be pointless and circular, since natural selection is part of the theory that explains how and why evolution happens (but not the only mechanism by which it operates). We do not define gravity by the proposed mechanism but by the observed behavior of matter.
If mutation stopped tomorrow evolution would continue, It would not end until there was no life left to vary. So using mutation to define evolution would be false.
Deniers will deny no matter how you change the definition, there is no reason to bastardize the science to try to please them. You would be better spending your time discussing things like ring species or asking them to define "kind." Likewise speciation is the outcome and subset of evolution and completely covered in the existing definition. If they have a problem with speciation let them argue with it, speciation is only one form of evolution. If nothing else it will encourage them to learn something about what they argue.

I’m not sure if that definition is much better.  That definition fits perfectly well with their (creationists) model.  As that article specified, they fully believe in adaptation.  Those ‘heritable characteristics’ to them, were there to begin with and don’t ‘arise’ later.

that means they don't believe in mutation which a whole new problem, and can be dealt with by searching for a paper on mutation.  Try chasing their definition instead of  letting them use a strawman to get you to twist the definition of evolution to suit what they want.

since the focus seems to be a non-scientific argument this site may help you. http://www.talkorigins.org/

adaptation and evolution are not really distinct concepts, paleontologists might talk about tetrapods adaptation to life on land which has continued for hundreds of millions of years. Unsurprisingly it sounds like the creationists you talk to don't understand the terminology they are using.

They do seem to understand their terminology, but just point out that many forms of 'adaptation' don't involve the creation of new genetic information.  That's why I was calling for a better definition that just a change in frequency.

John: BTW, please explain further your comment that "If mutation stopped tomorrow evolution would continue".  You have me intrigued.

This is a much more correct definition, especially since it carefully distinguishes evolution as an observable fact from theories of the causes of evolution.

@RunzWitScissors evolution will continue until there is is absolutely no variation within a population which basically means until the population is extinct or entirely clones. evolution only requires variation in heritable characteristics and it can continue until that variation is exhausted. Normally mutation keeps adding new variation but it takes time for evolution to spread/eliminate mutations. Also it is rarely all or nothing, balances of multiple competing mutations exist (Evolutionarily Successful Strategies) so some variation will also tend to be preserved.

@RunzWitScissors in line with John's comment consider a population with two virus-resistance alleles. Virus A is common, so resistance allele A spreads in the population, leading to less virus A and more virus B, so now resistance allele B is selected for and spreads through the population, etc. Continual evolution even in the absence of mutation.

reference for Evolutionarily Stable Strategies (yes I know my error)http://www.nature.com/scitable/knowledge/library/game-theory-evolutionary-stable-strategies-and-the-25953132?utm_source=%CE%B1%CE%B8%CE%B7%CE%9D%CE%95%CE%91+subscribers&utm_campaign=0a22881ee9-01_a8inea_Newsletter12_17_2013&utm_medium=email&utm_term=0_903691d971-0a22881ee9-116007725&ct=t(01_a8inea_Newsletter12_17_2013)

@John & iayork - Certainly variation & adaptation via natural selection can continue without mutations.  Yet in light of the central theme of this post:  to a creationist, those things are simply a part of their creation-model.  The distinction between that and evolution is issue:  The change in existing genetic material to pull from (via mutations).  It's like arguing with someone: 'look, I can shuffle these cards, therefore cards evolved'.  Their response:  'I believe cards can be shuffled as well.  It's the sufficient addition of new well-working cards that are in question'.

@RunzWitScissors 1. shuffling is a randomizing process, NS is not. 2. then their problem is with mutation, speciation, the fossil record, and the implication of evolution. each of which can be dealt with individually. Including accessory process like mutation in the definition makes it  inaccurate. They are basically saying, "I believe you can walk around the block but I don't believe it is possible for a person to walk across the country." the process is the same they are claiming that it somehow stops running after a certain amount of time. this is a claim THEY must provide evidence for.

@John  Yes, I get that NS is not random. Card shuffling is an analogy for genetic variation in a population.  Shuffling existing cards is different than shuffling AND creating new cards.  And your 'around the block' 'across the country' analogy isn't accurate.  More like selecting various pre-existing vehicles to travel with, vs. selecting AND creating new vehicles to travel with.

@RunzWitScissors no both mechanisms are identical the only difference is the size of the population and the run time. New vehicles for the genes are created with each generation, species are just arbitrary but useful groupings inside the one single population that is all life. Once time is considered all populations blur into each other, all life is one giant ring species.

@RunzWitScissors creationists mistakenly believe species is constant and concrete and not just an artifact how human need to think.  when you show them what species actually are they retreat to their own undefined term "kinds" which the claim (without evidence) does not change.


text:
Can drinking caffeine, alcohol and other diuretics be part of a good drinking regime?
Tea, Coffee, Beer, Coke etc…
I wonder if the benefit from amount of fluid we get from them is bigger or smaller then the handicap of dehydration. In other words it is worth to drink them if we want to have a good drinking regime?
Opening on the recommendation from this question.

A PlOS One study notes drinking moderate amounts of coffee (aka moderate caffeine intake) doesn't necessarily lead to dehydration. They note coffee has hydrating qualities akin to water. Further empirical studies found no substantial fluid loss in caffeinated beverages.
EDIT: Make note we're talking normal doses and moderate intake, though source 2 goes into diminishing effect of caffeine-mediated fluid loss in heavy/prolonged uptake.
The issue therein is with alcohol, which tends to inhibit vasopressin and the kidneys are constantly permeable to fluids.


text:
Why are the fertility rates of large predators kept low?
Predators at the top of a food chain, like lions, seem to have a relatively low fertility rate, which fits well to the ecosystem and avoids overpredation. But what is the mechanism that keeps the fertility rate at this level? A mutation that increases fertility among the predators should quickly spread in such a population, eventually leading to an increase of the predator fertility rate followed by overpredation. But it seems that this is usually not the case. What does keep the fertility rate of predators at the level they are?
P.S: Many explanations on population dynamics cite Lotka Volterra, including the existing question on Bio.SE "What prevents predator overpopulation?". Population dynamics predict orbits for the population sizes of predator and prey. Still this does not explain why the fertility rate of predators shouldn't go up.

@Remi.b - I don't think so, as that post deals with fertility rates in specific, not the population characteristics

This question is clearly not a duplicate of the linked question, and they have completely different perspectives. This question is dealing with the evolution of life histories, while the other one deals with dynamic predator-prey models and population regulation. The original poster has also already addressed the proposed duplicate in the question.

I think fertility rate (or rate of offspring production) is correlated to the size of the animal more than anything else.

Relevant:  http://science.sciencemag.org/content/349/6252/aac6284.full.
Also, see the NY Times article:  https://www.nytimes.com/2015/09/08/science/study-reveals-consistent-predator-prey-pattern.html

This question can be tackled in several ways, and also seems to contain a couple of misconceptions about ecological processes (e.g. problematic group-selection ideas and how species 'fit' into the ecosystem).
However, I think it is most usefully answered from the perspective of life history theory and the evolution of life histories (see e.g. Roff, 2002 and Fabian & Flatt, 2012 for background), and at its core this question is dealing with tradeoffs between e.g. fecundity and mortality.
All organisms act under a limited energy budget, and when they spend energy on some activities that will limit what they can spend on other activities.
This lies at the core of life history tradeoffs.
If organisms use resources to produce or raise offspring, this can therefore have consequences on growth, maintenance (e.g. disease resistance), predator avoidance and many other traits and behaviours, which all affect fitness.
With regard to your question, there is a well-known tradeoff between survival and fecundity (an intraindividual effect), which has been shown both between individuals within species and across species.
As with almost all species traits, there is also variability between individuals within populations, so that some have higher fecundity than others, but may then also suffer lower adult survival rates (maybe under certain environmental conditions).
Therefore, there are certainly already "mutation(s) that increases fertility among the predators" within populations.
The reason that fertility is not continuously increasing over time, is that this would have negative fitness consequences overall.
So the reason that the (average) life history traits within a particular species are what they are, is that they are the result of the evolutionary pressures that the species have been experiencing over evolutionary timeframes, acting on the entire life history.
To be sure, hypothetical mutations/changes in life history traits that would increase fecundity with no negative tradeoffs whatsoever for either parents or offspring (i.e. no negative fitness consequences) would definitely increase in frequency.
From the perspective of life history theory, everything else equal, selection will maximize the survival function, $l(x)$ (survival to age x) and the reproductive function, $m(x)$ (number of offspring at age x), while minimizing maturation age.
However, tradeoffs between these functions (directly or indirectly) and between generations will prevent these from being optimized one by one.
Population growth in long-lived predator species is usually also very sensitive to changes in adult survival rate (Caswell, 2001).
This result comes from analysing stage or age structured population models, and is the result of a life history strategy with high adult survival rates, fairly high age of maturation and relatively low fecundity.
To increase fecundity in this situation with negative effects on adult survival will risk adult death and therefore loss of many future years of reproduction.
These kinds of results and models are often used to identify which life history traits and stages to target in conservation actions, but can also be used to understand evolutionary processes, since population growth rate can be used as an overall measure of fitness.
It should also be added that many other ecological processes must be considered.
For instance, large predators often care for their young for quite a long time, since their hunting/foraging behaviours are complex, and it takes a long time for young to learn how to hunt successfully by themselves.
This means that adults need to bear not only the direct cost of producing offspring, but also to support them until they are self-sufficient.
For an extreme case, look at the Orangutan (not a predator, but still) where the young stay with their mother and nurse for up to 5-6 years (sometimes more).
To increase fecundity in such a situation will put a large strain on adult mothers.
You also need to consider trade-offs between offspring number and offspring "quality", which have been demonstrated in many species (an intergenerational effect).
This means that an increase in fertility can result in lower offspring survival over longer timeframes, with overall negative effects on fitness.
Finally, life history evolution is a large and complex field, and these are just a couple of examples and suggestions for why the situation isn't as simple as your question makes it out to be.
Environmental variability and the predictability of the environment are other factors that I haven't considered at all, but are both important processes when you try to understand life history evolution.
This question can also be approached from a more theoretical perspective, but without a background in life-history evolution I think it is more useful to start reading about different processes and tradeoffs, and then later if you are interested to go into the theory for a deeper understanding.

The way you pose the question, as in what mechanisms limit the fertility of predators, together with your comment about the distinction to the population dynamic aspect, strongly that what you are thinking about an adaptional mechanism here and asking about its characteristics, right?
In that case, this is very similar to the kind of scenarios you find in the group selection arguments: It is better for the species to limit it's fertility rate, thus a limit on the fertility rate will evolve.
This simplified view on group selection is heavily criticized as it's not a ESS: As you pointed our yourself, a mutation that would increase the fertility rate would quickly spread.
But this is assuming that an increased fertility rate actually would be beneficial. And what we forget here is that we have an inherent balance of quality of offspring versus number of offspring: r/K selection. The view that evolution constantly strives to produce increasingly ferocious eating and breeding machines is obviously too simplified if we were to just take a look at the how the biosphere actually looks like and compare it to our imagination how it could be like.
So basically, we could assume that the local "mechanism" here is simply r/K selection, with the population dynamics providing a major influence of the overall dynamics behind the scenes.

However, group selection do have an influence in the general evolutionary dynamics, as even the otherwise avid opponent Richard Dawkins agrees upon, but this is under the condition that group selection really is active natural selection of distinctive groups, and not some omniscient mechanism that somehow makes groups inherently develop traits that is beneficial to the group.
And the other conditions like ESS still have to be valid here as well: If a trait could be positively selected for by group selection, it has to 1. Be developed before any selection can act on it and 2. Stay a ESS for it to persist (unless the group selection pressure is very high, and I think it's generally assumed that it's many orders of magnitude less influential than selection within groups)
I could imagine the possibility that group selection somehow has had an influence in limiting the genetic pool of top predators to the kind that uses a this r/K strategy that we see today but here I digress, it's not needed to explain the fertility rate of the top predators we see today.

Downvoter, comment please?

In a given area there can be only a limited number of top predators due to resource limitations. Furthermore, as top predators are not hunted upon, a relatively high percentage of offspring will survive until adulthood. Hence, producing many offspring doesn't make sense as survival rates are high, while resources are limited. Producing many offspring is a huge investment of resources (must be raised and fed) and if in the end many offspring die of a lack of resources, then the invested effort and energy was for nothing. Hence, producing many offspring decreases fitness in top predators and this trait will have been selected against during evolution.

But it seems that this is exactly what lions do, that is produce a lot of offspring(about 6 cubs per lioness so 12 to 240 cubs for prides with 2 to 40 lionesses). This is all because the more offspring that are produced the more likely at least 1 will survive and because they live in large groups. If this isn't good for lions as a species since prey is getting more scarce than why didn't lions naturally go to extinction?

@Caters - "A lot of" is subjective. Rats have 10-12 per litter. That is what I would call a lot. Like rabbits they can also have multiple litters/annum.  Not what you would expect with lions.

The premise,

A mutation that increases fertility among the predators should quickly spread in such a population

probably isn't true if there are tradeoffs involved. It also wouldn't be true if the increased death rates due to overpopulation, were concentrated more heavily on the over-breeders and their descendants than on the rest of the population. (This could happen due to spatial structure; for example, if each predator hunted and raised their offspring in their own patch of territory.)

Tradeoffs are the bread and butter of Life History Evolution. ("Life History Traits" are the traits which are most directly related to fitness: age-specific mortality rates, age-specific fecundity rates, age of maturity, etc. Size is an honorary life history trait, as is offspring size, since these often have strong effects on mortality and fecundity.)
One of the most famous examples from the early days of Life History Theory is the Lack Clutch. The ornithologist David Lack noticed that birds never laid as many eggs as they were capable of producing, and wondered why. He proposed that there was a tradeoff between the number of offspring, and the survival of each of the offspring. He suggested that clutch size was designed to maximize the number of offspring who would survive to the age of fledging, and that this would be an intermediate optimum.$^1$

Lack was right that the most fit clutch size would be intermediate. In detail, clutches are usually a little smaller than he predicted, because there are additional tradeoffs he didn't consider.
Here's a partial list of tradeoffs that are often thought to be important.

The more offspring there are in this clutch, the less fit each of them will be. For example, maybe the parent has a certain amount of resources to invest in creating offspring; the more of them there are, the smaller each of them has to be (larger animals are often more fecund).$^2$ If the offspring fitness cost is in terms of survival, then this is Lack's hypothesis.
The more offspring there are in this clutch, the less likely the parent is to survive to future breeding seasons. (This is often called the Cost of Reproduction.) For example, maybe the parent has some resources which it has to allocate between reproduction, and its own maintenance. The optimum will not favor perfect maintenance (this is the basis of the Disposable Soma theory of aging);$^3$ but it also favors intermediate clutches (which are a little smaller the Lack Clutch).$^4$
The more offspring there are in this clutch, the less offspring there will be in future clutches. For example, maybe the parent has some resources which it has to allocate between reproduction now and its own continued growth (which will make it more fecund in the future). This tradeoff also has to do with the age at maturity: at what ages should an organism not reproduce at all (devoting all its resources to growth), and at what age should it start?$^5$

(Tradeoffs are often modeled as limiting-resource allocation problems, but this isn't the only sort of tradeoff. A tradeoff occurs whenever getting more of one good thing requires getting less of a different good thing [or more of a bad thing], for whatever reason.)
So much for life history tradeoffs in general. But, in the face of these tradeoffs (and whatever other constraints), which can have different strengths and different shapes etc., different species hit on different strategies. Why? What leads to the large predator life history being optimal for large predators? I don't know. I don't even know if it is known! Life History Evolution is complicated, with many different traits coevolving together. It's hard to model more than a few of these (holding the others constant) at a time. I don't know if anyone's actually explained the conditions that would lead to an entire suite of life history traits, from scratch. (Once upon a time, attempts would have been made to try and explain the large predator life history as one end of the r-selection vs. K-selection spectrum. But that paradigm has fallen out of favour among researchers.$^6$)
Here's another factor, elaborating on the first tradeoff, which I think is probably relevant.
If there are too many predators, then there won't be enough prey, and the average death rate will soon catch up. If this food-shortage cost were applied to every member of the predator population equally, then a mutation for increased fecundity would indeed be favoured: although all members shared the cost (increased mortality), only the mutant would get the benefit (increased fertility); on average the mutant would be fitter and the mutation would spread. (This is ignoring the other costs, such as slower parental growth and faster parental aging.)
But now suppose that you are a predator, and you own a patch of territory; and whether you over- or under-hunt mostly affects your territory. Suppose also that you care for your offspring, or at least let them share the hunting in your territory, rather than sending them elsewhere. Then over-breeding will lead to over-hunting in your territory (or, to hunting the same amount but having lower-quality offspring). But the other territories'll be fine! The brunt of the mortality cost of over-breeding and over-hunting, as well as the fertility benefit, will fall on you and your offspring.
tl;dr: if conditions (eg. due to the population's being spatially structured) are such that the mortality cost of higher fertility applies more strongly to the over-breeders than to the rest of the population, then there will be selection against over-breeding. In addition to costs of overpopulation, higher fertility can also negatively affect the parent's fitness in other ways; for example, by using up resources that they would have used for self-maintenance, or resources that they would have used for growth.

Lack, "The Significance of Clutch Size". Ibis (1947).
Smith and Fretwell, "The Optimal Balance Between Offspring Size and Number". American Naturalist (1974).
Kirkwood and Rose, "Evolution of Senescence: Late Surival Sacrificed
for Reproduction". Philosophical Transactions B (1991).
Charnov and Krebs, "On Clutch Size and Fitness". Ibis (1974).
Kozlowski, "Optimal Allocation of Resources to Growth and
Reproduction: Implications for Age and Size at Maturity". Trends in
Ecology and Evolution (1992).
Reznick, "r- and K-Selection Revisited: The Role of Population Regulation in Life-History Evolution". Ecology (2002).

The classic textbook for Life History Evolution is Stearns' The Evolution of Life Histories (1992). A more recent one (I haven't read it) is Roff's Life History Evolution (2002).

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
Is every evolutionary change an improvement?
In evolution, an species mutates and evolves over large genetic time spans. But every big change (say, swimming to walking, or walking to flying) is made up of countless micro-changes over the course of many many years and many many generations.
My question, does each "micro-change" have to be an improvement over the prior iteration? Or can evolution initiate micro-changes, knowing the current generation won't see the final result for an arbitrary number of years?

Related: http://biology.stackexchange.com/questions/13680/why-specifically-does-each-generation-on-average-improve-upon-the-design-of?rq=1

@arboviral Actually, that answer is enough to answer my question. I'm content calling this a duplicate question and closing it. Thanks for the link!

Delighted to help! I'd also suggest taking a look through https://en.wikipedia.org/wiki/Genetic_drift, and bearing in mind that 'improvement' depends on the environmental context, which changes constantly.


text:
3 flaws of genes from the perspective of a programmer

Having a double helix structure seems like a waste of space. In programming you would have a single array and just before mitosis you would double the single helix.
Having an exact copy of a cromatid. As long as a cromatit exists, there is always a possibility of errors, for example if something attacks the chromosome. So why isn't the cromatid just before mitosis, just like in 1? Then you are 100% certain, you have an exact copy. Again, a waste of space, but additionally vulnerability now.
Having 2 nucleobase (adenine, thymine, guanine, cytosine) seems like it makes more room for errors. Why aren't there just two of them? They are complementary anyways, so adenine for example can only be on the opposite side of thymine. Is it so that splitting is easier? In programming, if you only have to deal with a boolean value, you would always take it over having to deal with 4 values.

So, what does nature do different than a computer and why does it behave so differently?

Simple answer, nature doesn't think. all 3 flaws you mention are OK for natural selection as compared to some other ancient alternative, so nature preferred them.

Re #3. If you had only two, how would your alphabet code for the 20-odd amino acids?

Re #1: Would your alternative structure have similar chemical stability? Also, without redundancy, if there were any externally induced damages how would you rectify the damage?

Why? It's because it evolved that way. Period. Please refrain from asking broad open ended questions. Nobody ever claimed that this is the most perfect mechanism. However, it works quite well and it has not been selected out. Plus the double helix also allows for repairing errors. Please refine your question (keep it precise and fact based, and avoid asking multiple questions in a single post) or else it would be put on hold.

Moreover diploidy has its advantages for certain strategies. However, many organisms are haploid and they also survive well.

@curious_cat Yes, I address external damage in 3, where I find it bad that the 2nd chromatid is exposed to potential damage, which is, why I would only create it right before mitosis.

@kim366 I didn't get that bit. What if your single chromosome got damaged? How would you repair that?

@WYSIWYG Sorry, I don't know about any other amino acids. What I learned at school is, that the DNA consists of nucleobases, which form some kind of binary code. But the same could be achieved with just two of them and reading the DNA would be easier.

@curious_cat Well when one chromosome gets damaged, what happens in the real world right now? When a single chromatid gets damaged, how does the system know which of the 2 is correct? It seems like there would be a discrepancy, so which one is the right one?

@kim366 Please read about genetic code. It is not binary. It is 4 letter based and each word is 3 letters long. That way you can ensure that all amino acids are represented and you have synonymous codons too (which are sometimes helpful). If you want to achieve the same with a binary code, then your word length should be at least 5. Also read about DNA repair mechanisms. To fully answer your questions (as you explain in the comments) one might have to write a book. Therefore the question is broad. Please read these topics and ask a precise question.

@kim366 Without the redundancy of a 2nd strand you wouldn't even realize that there was a mistake, right? Error detection vs Error Correction.

@WYSIWYG Thanks! Either I understood it wrong or we are learning BS at school...

Either I understood it wrong or we are learning BS at school  -- or both.

Conversely, if errors never happen then neither does evolution.

Somewhat related with answers discussing the problems with computer/DNA analogies: Does DNA have the equivalent of IF-statements, WHILE loops, or function calls? How about GOTO? and Can DNA & RNA be considered as nature's programming language?

Wow, this question is quite controvertial... 5 upvotes and 5 downvotes...

You are missing the point that DNA is a better information storage system than the currently used computer equivalents on many levels: DNA stores Information more dense, more energy efficiently, and over much longer timescales. Only the I/O speed is slower. This means that DNA would be an ideal long-term data storage system even for conventional computers.

In programming, if you need to ensure your data has integrity, a single array won't do.
We have cyclic dependency checks in programing to determine if the data is corrupt.  We have hamming codes to permit detecting a fixing one bit corruption.  If we want to really fix multi-bit corruption, we need at least one copy of the data.  Lossless compression routines can make the storage of that copy smaller, but it doesn't remove the concept that the data is copied.
DNA provides a copy built-in, albeit a mirrored copy.  The structure of the molecule is such that the data is on the inside of a positionally stable backbone of sugars.  Since atoms and molecules are not likely to have the same sizes when attached to this sugar, a mirrored copy has the advantage of keeping the DNA molecule relatively stable in width along it's entire length.
This relative stability is also seen in the proteins that bind to and maintain the DNA.  Basically they don't require shapes that match a would-be rugged terrain of jagged connecting bits, only the ones that need to verify against the information require regions that match the "informational terrain" they are seeking.
It is really all chemistry at the lower levels, just at a very large scale.  Unfortunately (or fortunately) atoms don't come in standard sizes, with uniform properties much like bits in a computer do.  As such, the modeling of information crosses over from Biology to Computer Science well, but the implementation of it doesn't.
To get an appreciation of the differences between modeling and information in computation, look at the various lego / tinker toy / water computing modules.  You can build a computer out of anything; however, if you needed to integrate a lego logic module into a modern computer, it would be "less than elegant"

This addresses #1 but not #2 or #3. Though, the answers are all available in the question's comments.

The section including the statement "The structure of the module is such" indirectly hints why you don't have only two bases.  Two bases would require the same height relative to the DNA sugar backbone, or they would have to be "super bases" that spanned across to both sides of the backbone.  With the relative heights being the same, strands would (more easily) slip across each other, leading to new complexities. With "super bases" crossing both strands, the covalent bonds would make the two strands one, so there would be no duplicate.

Your individual questions here are reasonable enough - although you could do a bit better at knowing some more details of the systems you are dismissing as 'flawed'. However, I think the bigger problem is your overall approach, which seems to be to make analogies between human-made computers and biological systems and then critiquing the 'missing' parts from biological systems.
Analogies like this are necessarily limited. Take the idea of DNA as 'like' a hard-drive of a computer. The nucleotides = bits, genes = contiguous segments of memory, promoters = pointers, etc. Where does this model go wrong?
Well, hard-drives are not (currently!) self-assembling structures that read themselves by coding for more, smaller fragments of hardware that additionally compress or expand parts of the structure of the hard-drive according to which part needs to be read. Moreover they don't do this while floating in liquid and self-repairing from chemical and radiation damage.
There is an excellent book called "Cats' Paws and Catapults: Mechanical Worlds of Nature and People" by Steven Vogel that covers this kind of flawed analogical reasoning between biological and human-made systems. It's not that such analogies are useless, but it is very important to recognise the limitations and fully understand why certain design choices are made by biological systems due to the particular environmental and evolutionary constraints that they work under.


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When is Pheophytin used in electron transfer to the q cycle in plants?
If I understand it correctly:
(short version)
The Q cycle starts with PQH2 it removes the 2H(+) and gives one electron to Plastocyanin and the other to a stored Plastoquinone to charge it to Plastoquinone (-). This process is repeated and adds the electron to the stored Plastoquinone(-) to create a Plastoquinone (-2). This then gets 2H(+) to form PQH2. And the cycle begins anew.
But in a lot of pictures is shown that the excited P680 gives the electron to Pheophytin and that gives it to the Q cycle.
Please let me know when and where the Pheophytin interacts to give the electron.

Not sure if there is a definite answer. However, from structural studies, its proposed that essentially the Chlorophyll a and pheophytin(Pheo) are positioned ~10A away, and the transfer of a radical electron from the P680 center to Pheophytin does occur. The exact mechanism is possibly mediated by a tyrosine

Durant J.R et al (1995). Proceedings of the National Academy of Sciences of the United States of America, 92, 4798–4802
Oleg Victorovich (2006). Retrived:5/25/16. Site: http://samlib.ru/o/oleg_w_m/cdocumentsandsettingsolegmoidokumentythefunctionandthestructureofporphyrinmoleculesrtf.shtml

3.Zouni A et al (2001). Nature, 409, 739-743


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How to calculate relatedness in haplodiploid organisms (mainly full sisters and full brothers)?
I have tried to calculate the relatedness for haplodiploid organisms, but cannot understand the calculations behind full sister and full brother.

(taken from Wikipedia: haplodiploidy
I have managed to solve the other cases. Since female will have ZZ:

female will share 1/2 of her genes with daughter
female will share 1/2 of her genes with son
female will share 1/2 of her genes with mother
female will share 1/2 of her genes with father

Since male will have only Z:

male will share 1 (100%) of his genes with his daughter
male won't share anything with his son (unfertilized eggs leads to male in haplodiploidy system)
male will share 1(100%) of his genes with his mother
male won't share anything with his father (unfertilized eggs leads to male in haplodiploidy system)

Even though I figured out how others work, I wasn't able to calculate r for fullsister fullbrother? How can it be 3/4,1/4,1/2,1/2?

Is this a homework question? If so, you should sow us the work you've done so far, including the ones you already figured out.

The table is taken from Wikipedia: Haplodiploidy, right? If so, you should include a reference.

This is a relative thing in haplodiploidy system.

r = Mother's side + father's side(in this case)
D--Daughter
S--Son
F-Father
M-Mother

Note:

You can get the below things from the question image itself.(All that you need to do is to read male and female appropriately as son,father,mother,daughter based on the context.So simple!)

1.rMD=1/2
2.rMS=1/2
3.rDM=1/2
4.rDF=1/2
5.rFD=1
6.rFS=0
7.rSM=1
8.rSF=0
case 1:
rDD=(rDM*rMD) + (rDF*rFD)
rDD=(1/2*1/2)+(1/2*1)
rDD=3/4

case 2:
rDS=(rDM*rMS) + (rSF*rFD)
rDS=(1/2*1/2)+0*1
rDS=1/4

case 3:
rSD=(rSM*rMD) + (rDF*rFS)
rSD=(1*1/2)+(1/2*0)
rSD=1/2

case 4:
rSS=(rSM*rMS) + (rSF*rFS)
rSS=(1*1/2)+(0*0)
rSS=1/2

It is useful to think in terms of the proportion of genetic material that an individual gets from each parent and the amount of this genetic material that will be shared with another individual (on average), and then sum this together i.e. as:
$ relatedness = prop_{mother} * shared_{mother} + prop_{father} * shared_{father} $
A female will share 50% of the genetic material from the mother's side and 100% if the material from the father's side with her full sisters: $0.5*0.5 + 0.5*1 = 0.75$
A female will share 50% of the genetic material from the mother's side and 0% if the material from the father's side with her brother: $0.5*0.5 + 0.5*0 = 0.25$
A male will share 50% of the genetic material from the mother's side with his sister (and doesn't get any from the father's side).
A male will share 50% of the genetic material from the mother's side with his brother (and doesn't get any from the father's side).
Remember also that this only holds if the female has only mated once.

A female will share 50% of the genetic material from the mother's side and 100% if the material from the father's side with her full sisters--then it should be 0.50.5+11 right? and A female will share 50% of the genetic material from the mother's side and 0% if the material from the father's side with her brother: 0.5∗0.5+0*0 right?

A female still gets 50% of her genetic material from each parent, so it should be 0.5 in both calculations. The first case you suggest would lead to a relatedness of 1.25, which is clearly impossible.

Yes but are we using 0.5 because 1.25 is impossible?

No, because 50% (0.5) of the genetic material in a female comes from each parent.

okay relatedness=propmother∗sharedmother+propfather∗sharedfather Does this generalised statement apply in all cases other than haplodiploidy to calculate relatedness?

@ecolover In the general case it should hold, but I haven't considered it from all angles. Overall, you should be careful when thinking about/calculating relatedness in species with peculiar chromosomal makeups. The sex determining system is also important part, and there are in fact deviations from the general pattern for certain ancestral pairs (due to the inheritance of X & Y chromosomes). For instance, Paternal grandmothers are more closely related to their granddaughters than grandsons (see paper).

@fileunderwater I'm sorry for not informing you earlier but I've quoted your answer in a different post without your consent. After being pointed out by a senior user of that site, I realised I should have done this before. If you don't approve of this I'll flag it for closure.

@Tytoalba Forgot to answer this. No worries. That is ok.


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What causes sleep inertia?
Sometimes after a nap, I wake up in a state of grogginess. I learned that this is actually called sleep inertia. I'm able to find lots of methods to overcome this, but what causes it in the first place?

This is due to states of brain activation promoted by the reticular formation, specifically with the ascending reticular activating system that mediates the general activation of the upper areas, for sleep these areas should promote states of relative low activity promoting the cerebral activation profiles during sleep, it is also interesting to study the descending reticular activating system that mediates the general muscular activation that is generally inhibited during sleep (except clinical problems among others) (that is why you can not make muscular efforts when waking up, try an experiment: try to clench your fists strongly when you are awakening).

Try adding some sources to your points. It would help and form a much better answer.


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Why are bugs attracted to light bulbs but not flames?
Just now, a housefly was bugging me a lot and I wanted to get rid of it. My mother suggested me to turn off the lightbulb and off it went. However a small flame was burning in my room. Why didn't the housefly get attracted to the flame (although it was a luminous flame)?
My first guess is because of the high temperature a flame produces. This Wikipedia article states that:

A 300 watt tubular halogen bulb operated at full power quickly reaches a temperature of about 540 °C (1,004 °F)

And a charcoal fire can produce temperatures between 750–1,200 °C (1382-2192 °F). Source.
I've seen small bugs getting attracted to large halogen lights in sports complexes. Although these two temperature ranges are a sizable difference apart, 540 °C doesn't seem cool at all. Is it really due to temperature, or is there some other reason?

It could be something to do with them learning to avoid flames over many years. Fire is more of a natural occurrence than halogen bulbs. It could biologically coded within them to avoid flames. The same way the average human would run at the sight of a tarantula, but doesn't get hugely scared if they just saw a plastic model of a spider, insects may know innately that fire is harmful, but newer man-made sources of light may not invoke the same biologically-driven behavioral response.

Not all animals are attracted to light but those that are are motivated by mating behavior. Some female moths will seek a light source and release pheromones drawing the males to the light. The old stories about a moth flying into a flame is a myth

It has nothing to do with temperature, but with the color of the light. Insects are especially attracted to lights with short wavelenghts, especially Ultraviolet (UV) light. Yellow and red light attract far less insects. (Rich & Longcore 2006). PDF
White light, like the one emitted by an average light bulb, consists of a broad spectrum, that includes UV. Halogen lights also emit UV. Flames on the other hand emit mostly red and yellow light.
Unless the flames burn at a very high temperature, then the flames turn blue. To illustrate this, my parents used to have a candlelight on gas they used during camping. This light burnt at a very high temperature, and hence the flame had a bluish color. And it did attract insects, especially moths.


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Clonal and subclonal mutations in cancer
Likely this will sound as a silly question, but I don't know whom to ask. Ok, here it goes.
McGranahan et al link found that tumors with high clonal antigen load are more likely to respond to the immune checkpoint blockage.
Bozic et al link (at the same time) proposed a probabilistic model to determine the number of clonal and subclonal mutations. They found that fast growing cancer types (where growth is cell division over cell death) are more likely to generate a higher number of subclonal mutations.
Knowing that melanoma and NSCLC are fast growing cancer types, these results seem to contradict each other. We know that they are fast and we know that both melanoma and NSCLC are cancer types where immunotherapy is most effective.
Plus, probabilistically how likely it is for a certain cancer lesions to have lots of shared mutations? A totally genetically homogeneous lesion with no branches with a very high number of mutations.
Or (much more likely) I'm missing something.

Your question is not fully clear. Are you interested in knowing what is the rate of mutation in different cancer types? If so, here is a good starting point https://www.nature.com/nature/journal/v502/n7471/full/nature12634.html

@alec_djinn rather than the rate of mutations, how mutations are distributed in a single lesion.

That depend on the cancer type. Most of the mutations are randomly distributed but some tend to cluster. Check the paper I have linked before. The question is maybe too broad, have a look here http://www.nature.com/tcga/

@alec_djinn (1/2) I will read it, thanks.
But that's the thing, the first paper states that higher number of clonal mutations (present in all cancer cells) rather than subclonal (present in a subgroup of cancer cells) is more favorable for the treatment. The second paper states that fast growing cancer types are more likely to develop subclonal mutations rather than clonal.

@alec_djinn (2/2) Melanoma (for example) is a fast growing cancer, which should develop more subclonal mutations (following the model of the second paper) and should have worse outcome for the treatment (following the results of the first paper). But it's the one showing the best outcome.

I will read the paper you cited and try to answer. In general however, there are some common mutations present in different cancers, like TP53 or BRCA mutations. These mutations are often the "founder" mutations, i.e. they occur at the beginning, so all the other generations will have it as well. If you have a therapy targeting those mutations then the majority of the cancer cells should be sensible to it.

However, fast growing cancers have a higher chance of accumulating new mutations that may confer RESISTANCE to the therapy to a few subclonal population. This will end up in a relapse since the few resistant cells will grow over time and the nasty cycle continues.

@alec_djinn Yes that's what I knew for traditional chemo or targeted treatment. Immunotherapy however targets the immune system. Fast growing highly mutated cancers become more likely to cause immune response via their higher neoantigen load. Now this hypothesis perfectly fits with what we know: indeed we see immunotherapy to work better for melanoma or NSCLC rather than, say, prostate. That's why I cannot fit the first paper into the story.

I have read the papers and I can see you point. But again, in the second paper the authors use data from colorectal cancer. I would not generalize their findings assuming they are valid for other cancer types.

@alec_djinn thank you for the time spent reading the two papers, not everybody is willing to do it ;-) I also thought the the discrepancy could be given by the cancer type, but formula (6) where they estimate the number of clonal mutations seems to be dependent only on the growth rate. Which is the point of my question. I get that they tested only for CRC, but it fits so well that I'm wondering what's the probability that the model does not work for other cancer types. I guess very low.

They don't really contradict each other; the clonality of neoantigens adds an extra layer over the overall mutational load ; but overall mutational load (and consequently overall neoantigen burden, because mutations in a fixed sequence context generate neoantigens at a certain rate) by themselves are also predictive.
Tumours with a high neoantigen burden tend to generally respond better anyway (See Snyder et al, NEJM , Rizvi et al, Science and Van Allen et al, Science) and also the fact that mismatch repair deficient cancers generally tend to respond.
So if you take high neoantigen tumours already (both lung and melanomas have extremely high mutation burdens), and then stratify them based on neoantigen clonality , you tend to further stratify patients by response associating with checkpoint blockade.
The whole point of McGranahan et al was to point out that it is possible to raise immune responses that target all the cells in a tumour, and that the ability to do this may dictate survival in general and checkpoint blockade response in particular, but only when antigen presentation and immune infiltration are intact. McGranahan et al note that clonal neoantigens do nothing in Lung squamous cancers because of depleted CD8 infiltration and MHC class I expression and there is other work (See http://www.pnas.org/content/113/48/E7759.abstract )
As for this bit

Plus, probabilistically how likely it is for a certain cancer lesions
  to have lots of shared mutations? A totally genetically homogeneous
  lesion with no branches with a very high number of mutations.

That is quite complicated to answer; but as a general rule it is worth remembering that,  as the number of divisions and the number of mutations (and ergo prospective subclones) increases, for tumours to stay clonal it requires extremely strong selective sweeps so that at any given time a very small number of clones tends to predominate, and at least in untreated/pre-treatment tumours, there is the tendency for a lot of them to show effectively neutral patterns of evolution ( http://www.nature.com/ng/journal/v48/n3/abs/ng.3489.html )


text:
Is there any species (subespecies?) whose DNA or RNA contains only in stem-loop structures?
I am a computer-scientist working in nucleic acids from the perspective of Formal Language Theory. I am dealing with several papers that analyze the Stem-Loop structure of DNA.
I would like some solid reference that confirms (or negates):
1) The existence of these structures
2) The existence of a (species, subespecies, ...) whose DNA or RNA has been confirmed to have strings which are a pure stem-loop structure.

The existence of stem loops is well established, so I'm not sure what kind of paper you're looking for in that regard. There are even crystal structures. As for your second question, it seems exceedingly unlikely.

I'm not sure this analogy will be helpful (and I'm sure someone could poke holes in it), but you are somewhat asking for 1) Confirmation that a car with wheels exists, and then 2) Asking if there is a car that is only wheels.

@BryanKrause I'll cite you :D

The existence and importance of stem loop structures is well documented.
I'm not sure what the second question means exactly. If you mean 'an organism that has at least one stem loop, try HIV. The MS2 system is also a stem loop binding protein that's used as a tool a lot in biology, I suggest googling it.

The second question means if there is any organism whose DNA or RNA has been documented to have at least one strand which is only a stem-loop, not a stem-loop contained within a larger strand. I suppose the answer is no.

But anyway, I would like to cite a paper stating something like  " (...) it has been observed that stem-loops always appear embeded in larger strands (...)"

@D1X Ah I think I might realize now your misconception...stem-loops are typically quite short relative to genomes, from a few base pairs to a few tens of base pairs, versus even viral genomes that are at least several thousand bases and the smallest bacterial genomes in the 100s of thousands. You won't find a citation saying what you state because it's sort of obvious from the underlying math: you could find some papers on the largest stem-loop structures and smallest genomes to make your point, though.


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16s rRNA Sequencing From Gut Microbiome (stool)
Do I extract RNA or DNA from gut microbiome (stool samples) if want to do 16s rRNA sequencing?

Genomic DNA, see Illumina's own protocol.

More correctly: you are sequencing 16s rDNA, the gene coding for the 16S rRNA.

Well, it depends on what you want to know. If you want to find who is there, then DNA, if you want to know who is there and their functionality, then RNA. Though if you have a lot of samples and limited budget, 16S rRNA is a cheap reliable option.

In general, you extract DNA, then PCR out the 16rRNA coding regions and finally sequence them.
Here some links
http://press.igsb.anl.gov/earthmicrobiome/protocols-and-standards/16s/
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0068739
https://support.illumina.com/content/dam/illumina-marketing/documents/products/other/16s-metagenomics-faq-1270-2014-003.pdf


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mtDNA sequencing error rates
What is the method to estimate the error rate in mtDNA sequencing?
E.g. we are given a profile like this:
16069T 16126C 73G 152C
Is there a method, a table of known values, or any other way to say that there is an x% chance of 16069T being incorrect?
I'm asking because I would like to have a method to estimate the probability of the profile being incorrect. Something like:
Perr = Perr(16069T) X Perr(16126C) X Perr(73G) X Perr(152C)


text:
What Species of Duck/Waterfowl is this
I snapped a picture of this beaut in Irvine, CA, but I do not believe it is in the list given in "Ducks at a Distance: A Waterfowl Identification Guide" by Bob Hines (http://www.gutenberg.org/ebooks/18884). What species of duck is this? I believe it has the shape of a duck, but the colors are unfamiliar to me.

Credit to @Sᴋᴜʟʟᴘᴇᴛʀᴏʟ for providing the link to the pdf

I think this is an Egyptian Goose (Alopochen aegyptiacus), see the image (First from the Wikipedia article, the second from here, interestingly taken in Irvine):

You can see the distinctive spot around the eye and also the colored feathers on the back. Also, this bird is too big to be a duck.


text:
What is the cause of singularity in phase response curve of human sleep and bright light?
I have no background in science as I am a computer programmer, but I am trying to get information on phase response curves, human sleep and exposure to bright light as I am trying to get to grips with a possible diagnosis of delayed sleep phase disorder.

The point marked phase delay is typically where someone would go to sleep and they would typically wake a couple hours after the peak marked phase advance. What is the physiological process that cause the move from delay to advance via the point marked singularity? In other words, why does light cause a delay initially but then cause an advance?


text:
BLAST 16s rDNA: Everything alligns to Bacillus sp
I'm trying to identify and isolate Pseudomonas and Bradyrhizobium strains from rhizosphere and endophytic compartments of a tree. I plated the strains on selective media (King's B. for Pseudomonas and YEM for Bradyrhizobium) and purified the colonies grown by picking them and let them grow on separate plates. There is a lot of variety in morphologies.
When I had grown colonies of bacteria and was able to pick up single colonies I did a 16s rDNA pcr reaction on the colonies. After sequencing the 16s rDNA sequence I did a quick BLAST search to annotate the sequences and thus the bacteria to a species. But from all colonies till now (already 25 reactions sequenced) I only found that they allign best to Bacillus. I think this is strange because there should be a lot of variety according to their morphology.
Does anyone has a solution for this? Is it, for example possible to exclude the Bacillus group from the BLAST search? Or does this mean my colonies are still contaminated?
This is probably not enough information to answer my question but maybe you have experience with doing this and have some ideas.
Thanks!

As you guessed in the last sentence, it's hard to answer your question without some more information.
For example, how much of the 16S rRNA gene did you sequence? Most people just sequence the v3-v4 regions which are the most variable, but they only comprise a few hundred bases. Sequencing the entire 1500 bp gene gives more specificity when doing searches, but is also more expensive.
With regards to selection of colonies, do you know from the literature how selective your two different types of media are? We did similar studies with environmental E. coli including several rounds of screening and selection, and you would be surprised how much "junk" we got. If you're isolating from soil, Pseudomonas and Bacillus spp. are going to both be highly prevalent.
With that in mind, it is not enough to grow on selective media, do one transfer, and call it good. Really, you should have a screening method in addition to your selective media. Maybe some compound that yields fluorescence or a visible change to the surrounding media that you can use to differentiate your target bacteria from contaminants. Once you have your candidates, you need to isolation streak them onto fresh selective media. Then screen again and isolation streak again. Repeat this process 2-3 times, or until you have monomorphic, pure cultures. Only then should you start doing genetic/genomic testing on your isolates.
Hope this helps.

Sorry, just realized this was asked 7 months ago. My two cents though, for what it's worth.


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Is human genome editing more complicated than mice genome editing?
A lot of genome editing trials are being done on mice and rats, but I often read that the step from mice- to human genome editing is complicated. What are precisely? I guess it has something to do with human's more advanced immune system, but I would like to have a better general understanding of this.


text:
Elastic arteries and muscular arteries supplying to upper body
What are the elastic arteries and muscular arteries supplying blood to upper body (above heart) in humans?


text:
From which animal is this derived?
I know only that it is not a bird and an insect. I am really interested in from what it is.

It looks like it could be some sort of internal cartilage, do you know anything more of its origin (where/when it was found, where was the picture taken, in what context...), i.e. could it be a sea creature for example?

Yeah it could be a sea creature.

Sorry it is a sea creature but it isn't a fish.

Please crop your image and specify the origin of the object.

I concur with the answer of Gerardo Furtado. I sometimes go fishing and this looks exactly like the gladius (Fig. 1) of a loligo vulgaris (Fig. 2). The gladius is a hard internal structure in cephalodopds. In French we call that a "plume", as it has the appearance of a feather.

Fig. 1. The gladius, or squid pen. source: Oceans of Kansas

Fig. 2. loligo vulgaris. source: wikipedia