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8

You can access the Imperial College global population dynamics database. They will have time series data at specific locations. http://www3.imperial.ac.uk/cpb/databases/gpdd There is a sister database as well that might be useful. http://lits.bio.ic.ac.uk:8080/litsproject/ These contain several hundred time series, and you can see a paper that used them ...


8

I think it does make sense - with a population density for finland that is so low, the disease with such a low beta cannot communicate to enough people to propagate. The number of people who have this disease will be fewer each week. I think this makes sense because at 16 / km^2, you can expect that practically nobody will ever see each other. This is ...


5

Well, I think I found the very simple mistake I made… Looking again in my equations, I realize that (for some reason) $cor = 2 \cdot \frac{\sigma_A^2}{\sigma}$ And looking at this website, I see that the slope of the parent-offspring regression is $\frac{h_N^2}{2} = slope$ Here was my mistake!


5

I guess you meant the population size stability. It is considered that the biosystems will increase their capacity of adaptation when evolving in very fluctuating environments. I believe the population stability is embedded in the adaptability of individuals. There is a measurement about it, evolvability, when the environment changes, the faster the ...


5

Your calculations are the following. Assuming non-overlapping generations, the number of ancestors you have in the last $t$ generation is given by: $$\sum_{i=1}^t 2^t$$ This sounds correct. But there are some very strong assumptions: Generations are non-overlapping. A more realistic model would need to consider $t$ as a continuous variable a give a ...


5

Here is my full derivation to the book example you gave, hopefully it'll help you clear up what went wrong: You need to remember that after there is selection acting on the population, you no longer have a total of 1 after selection. Think of selection as "killing" individuals, which means the total is now 1 minus what has been "selected out". s*y is what ...


4

It is certainly possible as yes, rapid population growth will reduce LD. From Slatkin, 1994: In a rapidly growing population, however, there will be little chance of finding significant nonrandom associations even between completely linked loci if the growth has been sufficiently rapid. Or Nature Reviews, 2002 Przeworski... showed that population ...


4

1 billion hives (at 10,000-50,000 bees/hive this is 10-50 trillion bees) Managed: 100 million hives Based on country-level data from FAO, supplemented for a few countries with Apiservices, in 2011 there were about 80 million managed hives. Because FAO lacks any data for some countries, and other countries under-report (for instance US figures don't ...


4

You can use power analysis to work out answers depending on the specifics of your data. The things you need to consider are: The power of the test. This is the probability that the test will fail to reject the null hypothesis even if in truth it is false (Type II error). If the population is not in equilibrium, what is the probability that the test will ...


4

After talking to my teacher, he said that biological control is the introduction of species to control another species, however species may be introduced for other reasons (the "Introduced Species" method), such as to "assist an ecosystem cope, flourish or re-establish itself." The example he gave was the introduction of South African veldt grass to ...


4

An easy way to visualize the mistake in your thought experiment is to consider a bottleneck event, when the ancestral population was very small, maybe just a few individuals. This would mean that the entire current population is descending from just a few individuals. Your thought experiment is assuming that the "pyramid" of your ancentors is expanding all ...


3

To a good first approximation $\overline{\Delta f} = 0$. Where $\overline{\Delta f}$ is the mean change in fitness down to any point or indel mutation. The reasons for this are as follows: In the genome of higher organisms, most of the genome is non-functional ("junk") so most mutations will not have any effect regardless of the change made. A substantial ...


3

According to Hartl & Clark on population genetics: "Population genetics deals with Mendel's laws and other genetic principles as they apply to entire populations of organisms.... also includes the study of the various forces that result in evolutionary changes in species through time." According to Conner & Hartl also on population ...


3

I don't believe you can produce a general function for this. It will depend on the exact gene and organism you are considering. From a molecular point of view, the vast majority of recessive mutations result from a change producing either a non-functional protein product or a truncated product that is cleaned up by the cell. We can reasonably assume that ...


3

Mendel published his results 1866 but they were rediscovered only in 1900. The Hardy-Weinberg model is an application of Mendel's rules to a population that is not under selection forces. So the one builds on the other, and Hardy-Weinberg is a simplification model-wise, and Mendel's rules are not detailed enough either. It's the same relation as with a ...


3

Biological control does not have to be with an introduced species. It can also be accomplished by either artificially inflating the number of existing predators. E.g. Spruce bud worm has a natural predator in the form of a tiny wasp. But budworm can spread through a stand faster than the wasp can. By moving popluatins of the wasp to the forefront of the ...


2

*It's been several years since I've worked with similar equations. The following reply is based on memory, and if anyone has a firmer grasp of the materials, please modify or answer as you see fit. I would assume λ is under ideal conditions or as an average of whatever species you're working with. Your modified lifetime reproductive output still leaves out ...


2

Everybody said it already, but there is none. The original HWE equation ($(p+q)^2=1$) works because you've got two variables and two equations ($p+q=1$) to work with (in reality, these are just one equation and one variable, since $q=1-p$ so $p+(1-p))^2=1$). Now you have three variables and still only the one equation ($p+q+r=1$) which is, mathematically, ...


2

Genetic drift is the change of allele frequencies in a population due to random sampling during reproduction. This can cause some allele combinations to become more or less common than would be expected without drift, thus creating a disequilibrium. However, it's the combination of genetic drift AND selection that generates negative (instead of positive) ...


2

I'll kick this off with an attempt at a definition: a selection regime is the set of selective pressures on a population As in, "alteration of selection regime resulting from harvest" (Mooney & McGraw 2007) or "Human-induced nutrient input can change the selection regime and lead to the loss of biodiversity. For example, eutrophication caused ...


2

Is this the exact text from the book? The left side seems to represent the probability for "No coalescence in $k$ lines in $t$ generations (i.e. the $Pr(k)^t$ term), and at least one coalescence among those lines in generation $t+1$ (the $1-Pr(k)$ term)" which is the same event as "First coalescence event in $k$ lines is exactly in generation ...


2

As long as members of a generation "randomly choose" their ancestor in the previous generation the law of independent probability (your equation) will hold. Any study of coalescent theory begins with the Wright-Fisher model. The assumptions are: finite diploid population of constant size N, non-overlapping generations (simultaneous reproduction), random ...


2

The genetic load is a population construct, a way of quantifying the fitness reduction in a population due deviations from the optimal genotype. One type of load comprises all the others, namely genetic load. Genetic load is just the loss of mean fitness relative to the ideal fitness. You forgot to include substitution load in your list, but ...


2

For some statistical models, see e.g. The Mathematics of Biodiversity (Part 1) by John Baez. However, in terms of new species, such statistical methods may not suffice - usually also our tools for discovering change. So it might be more accurate to extrapolate number of some kind species we know as a function of time. For extrapolations (though not for ...


2

Your question leads to the field of structured population models, and I am afraid the answer is: It very much depends on the exact Leslie matrix!. The population may go extinct, may remain at stable size or may grow indefinitely (theoretically speaking), may increase in size and then go extinct, etc… This is at least true if you make the assumptions that ...


1

If the fitness of a heterozygote is $(1+s/2)$ and of a homozygote is $(1-s/2)$ then why is the probability for a given state $(1+s/2)^j(1-s/2)^{m-k}$ $$\binom mj (1/2)^j(1/2)^{m-j}= \binom mj (1/2)^m~~ ?$$ As you pointed out earlier, in the general case it need not be true that $p = q = 1/2$ but that is what the form of the probability above implies. So ...


1

[This is purely speculative] Assumptions: impact on fitness is measured by survival chance impact is because of protein coding genes Probability of a mutation at position $i$ $P(m=i\ |\ g)$ where $g$ is the genome with its annotations. Probability that activity of some protein changes by X-fold given mutation at $i^{th}$ position(s) in the genome: ...


1

To derive it, first use that $E[x(1-x)]= E[x-x^2]=E[x]-E[x^2]$ and that $E[x^2]=\text{Var}[x]+E[x]^2$ to rewrite the left-hand side: $$E\left[x_{t+1}(1-x_{t+1})\right] = E\left[x_{t+1}\right](1-E\left[x_{t+1}\right])-\text{Var}\left[x_{t+1}\right].$$ The equation for $p_{ij}$ is just saying that $2Nx_{t+1}$ is binomially distributed with $2N$ trials with ...


1

The notation at this site resembles that in your question but preserves the $\frac{x_t}{2N}$ notation for probability of selecting an allele. $$E[\frac{x_{t+1}}{2N})(1 - \frac{x_{t+1}}{2N} )|x_t] = (\frac{x_{t}}{2N})(1 - \frac{x_{t}}{2N}) (1 - \frac{1}{2N}) $$ The expression $(\frac{x_{t}}{2N})(1 - \frac{x_{t}}{2N}) $ is the probability of heterozygosity ...


1

I will Suggest: Selective Breeding in Aquaculture: An Introduction Authors: Trygve Gjedrem, Matthew Baranski ISBN: 978-90-481-2772-6 (Print) 978-90-481-2773-3 (Online)



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