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Friday, May 18, 2018

Cladistics

From Wikipedia, the free encyclopedia
Cladistics (from Greek κλάδος, klados, i.e., "branch")[1] is an approach to biological classification in which organisms are categorized in groups ("clades") based on the most recent common ancestor. Hypothesized relationships are typically based on shared derived characteristics (synapomorphies) that can be traced to the most recent common ancestor and are not present in more distant groups and ancestors. A key feature of a clade is that all descendants stay in their overarching ancestral clade. Radiation results in the generation of new subclades by bifurcation.[2][3][4][5]

The techniques and nomenclature of cladistics have been applied to other disciplines. (See phylogenetic nomenclature.)

History

Willi Hennig 1972
Peter Chalmers Mitchell in 1920
 
Robert John Tillyard

The original methods used in cladistic analysis and the school of taxonomy derived from the work of the German entomologist Willi Hennig, who referred to it as phylogenetic systematics (also the title of his 1966 book); the terms "cladistics" and "clade" were popularized by other researchers. Cladistics in the original sense refers to a particular set of methods used in phylogenetic analysis, although it is now sometimes used to refer to the whole field.[6]

What is now called the cladistic method appeared as early as 1901 with a work by Peter Chalmers Mitchell for birds[7][8] and subsequently by Robert John Tillyard (for insects) in 1921,[9] and W. Zimmermann (for plants) in 1943.[10] The term "clade" was introduced in 1958 by Julian Huxley after having been coined by Lucien Cuénot in 1940,[11] "cladogenesis" in 1958,[12] "cladistic" by Cain and Harrison in 1960,[13] "cladist" (for an adherent of Hennig's school) by Mayr in 1965,[14] and "cladistics" in 1966.[12] Hennig referred to his own approach as "phylogenetic systematics". From the time of his original formulation until the end of the 1970s, cladistics competed as an analytical and philosophical approach to phylogenetic inference with phenetics and so-called evolutionary taxonomy. Phenetics was championed at this time by the numerical taxonomists Peter Sneath and Robert Sokal and the evolutionary taxonomist Ernst Mayr.

Originally conceived, if only in essence, by Willi Hennig in a book published in 1950, cladistics did not flourish until its translation into English in 1966 (Lewin 1997). Today, cladistics is the most popular method for constructing phylogenies from morphological and molecular data. Unlike phenetics, cladistics is specifically aimed at reconstructing evolutionary histories.

In the 1990s, the development of effective polymerase chain reaction techniques allowed the application of cladistic methods to biochemical and molecular genetic traits of organisms, as well as to anatomical ones, vastly expanding the amount of data available for phylogenetics. At the same time, cladistics rapidly became the dominant set of methods of phylogenetics in evolutionary biology, because computers made it possible to process large quantities of data about organisms and their characteristics.

The way for computational phylogenetics was paved by phenetics,[15] a set of methods commonly used from the 1950s to 1980s and to some degree later. Phenetics did not try to reconstruct phylogenetic trees; rather, it tried to build dendrograms from similarity data; its algorithms required less computer power than phylogenetic ones.

Methodology

The cladistic method interprets each character state transformation implied by the distribution of shared character states among taxa (or other terminals) as a potential piece of evidence for grouping. The outcome of a cladistic analysis is a cladogram – a tree-shaped diagram (dendrogram)[16] that is interpreted to represent the best hypothesis of phylogenetic relationships. Although traditionally such cladograms were generated largely on the basis of morphological characters and originally calculated by hand, genetic sequencing data and computational phylogenetics are now commonly used in phylogenetic analyses, and the parsimony criterion has been abandoned by many phylogeneticists in favor of more "sophisticated" but less parsimonious evolutionary models of character state transformation. Cladists contend that these models are unjustified.[why?]

Every cladogram is based on a particular dataset analyzed with a particular method. Datasets are tables consisting of molecular, morphological, ethological[17] and/or other characters and a list of operational taxonomic units (OTUs), which may be genes, individuals, populations, species, or larger taxa that are presumed to be monophyletic and therefore to form, all together, one large clade; phylogenetic analysis infers the branching pattern within that clade. Different datasets and different methods, not to mention violations of the mentioned assumptions, often result in different cladograms. Only scientific investigation can show which is more likely to be correct.

Until recently, for example, cladograms like the following have generally been accepted as accurate representations of the ancestral relations among turtles, lizards, crocodilians, and birds:[18]

  
Testudines   turtles

Diapsida   
Lepidosauria   lizards

Archosauria
Crocodylomorpha   crocodilians

Dinosauria birds




If this phylogenetic hypothesis is correct, then the last common ancestor of turtles and birds, at the branch near the lived earlier than the last common ancestor of lizards and birds, near the . Most molecular evidence, however, produces cladograms more like this:[19]

Diapsida   
Lepidosauria   lizards

Archosauromorpha
Testudines   turtles

Archosauria  
Crocodylomorpha   crocodilians

Dinosauria birds




If this is accurate, then the last common ancestor of turtles and birds lived later than the last common ancestor of lizards and birds. Since the cladograms provide competing accounts of real events, at most one of them is correct.

Cladogram of the primates, showing a monophyletic taxon (a clade: the simians or Anthropoidea, in yellow), a paraphyletic taxon (the prosimians, in blue, including the red patch), and a polyphyletic taxon (the nocturnal primates – the lorises and the tarsiers – in red)

The cladogram to the right represents the current universally accepted hypothesis that all primates, including strepsirrhines like the lemurs and lorises, had a common ancestor all of whose descendants were primates, and so form a clade; the name Primates is therefore recognized for this clade. Within the primates, all anthropoids (monkeys, apes and humans) are hypothesized to have had a common ancestor all of whose descendants were anthropoids, so they form the clade called Anthropoidea. The "prosimians", on the other hand, form a paraphyletic taxon. The name Prosimii is not used in phylogenetic nomenclature, which names only clades; the "prosimians" are instead divided between the clades Strepsirhini and Haplorhini, where the latter contains Tarsiiformes and Anthropoidea.

Terminology for character states

The following terms, coined by Hennig, are used to identify shared or distinct character states among groups:[20][21][22]
  • A plesiomorphy ("close form") or ancestral state is a character state that a taxon has retained from its ancestors. When two or more taxa that are not nested within each other share a plesiomorphy, it is a symplesiomorphy (from syn-, "together"). Symplesiomorphies do not mean that the taxa that exhibit that character state are necessarily closely related. For example, Reptilia is traditionally characterized by (among other things) being cold-blooded (i.e., not maintaining a constant high body temperature), whereas birds are warm-blooded. Since cold-bloodedness is a plesiomorphy, inherited from the common ancestor of traditional reptiles and birds, and thus a symplesiomorphy of turtles, snakes and crocodiles (among others), it does not mean that turtles, snakes and crocodiles form a clade that excludes the birds.
  • An apomorphy ("separate form") or derived state is an innovation. It can thus be used to diagnose a clade – or even to help define a clade name in phylogenetic nomenclature. Features that are derived in individual taxa (a single species or a group that is represented by a single terminal in a given phylogenetic analysis) are called autapomorphies (from auto-, "self"). Autapomorphies express nothing about relationships among groups; clades are identified (or defined) by synapomorphies (from syn-, "together"). For example, the possession of digits that are homologous with those of Homo sapiens is a synapomorphy within the vertebrates. The tetrapods can be singled out as consisting of the first vertebrate with such digits homologous to those of Homo sapiens together with all descendants of this vertebrate (an apomorphy-based phylogenetic definition).[23] Importantly, snakes and other tetrapods that do not have digits are nonetheless tetrapods: other characters, such as amniotic eggs and diapsid skulls, indicate that they descended from ancestors that possessed digits which are homologous with ours.
  • A character state is homoplastic or "an instance of homoplasy" if it is shared by two or more organisms but is absent from their common ancestor or from a later ancestor in the lineage leading to one of the organisms. It is therefore inferred to have evolved by convergence or reversal. Both mammals and birds are able to maintain a high constant body temperature (i.e., they are warm-blooded). However, the accepted cladogram explaining their significant features indicates that their common ancestor is in a group lacking this character state, so the state must have evolved independently in the two clades. Warm-bloodedness is separately a synapomorphy of mammals (or a larger clade) and of birds (or a larger clade), but it is not a synapomorphy of any group including both these clades. Hennig's Auxiliary Principle [24] states that shared character states should be considered evidence of grouping unless they are contradicted by the weight of other evidence; thus, homoplasy of some feature among members of a group may only be inferred after a phylogenetic hypothesis for that group has been established.
The terms plesiomorphy and apomorphy are relative; their application depends on the position of a group within a tree. For example, when trying to decide whether the tetrapods form a clade, an important question is whether having four limbs is a synapomorphy of the earliest taxa to be included within Tetrapoda: did all the earliest members of the Tetrapoda inherit four limbs from a common ancestor, whereas all other vertebrates did not, or at least not homologously? By contrast, for a group within the tetrapods, such as birds, having four limbs is a plesiomorphy. Using these two terms allows a greater precision in the discussion of homology, in particular allowing clear expression of the hierarchical relationships among different homologous features.

It can be difficult to decide whether a character state is in fact the same and thus can be classified as a synapomorphy, which may identify a monophyletic group, or whether it only appears to be the same and is thus a homoplasy, which cannot identify such a group. There is a danger of circular reasoning: assumptions about the shape of a phylogenetic tree are used to justify decisions about character states, which are then used as evidence for the shape of the tree.[25] Phylogenetics uses various forms of parsimony to decide such questions; the conclusions reached often depend on the dataset and the methods. Such is the nature of empirical science, and for this reason, most cladists refer to their cladograms as hypotheses of relationship. Cladograms that are supported by a large number and variety of different kinds of characters are viewed as more robust than those based on more limited evidence.

Terminology for taxa

Mono-, para- and polyphyletic taxa can be understood based on the shape of the tree (as done above), as well as based on their character states.[21][22][26] These are compared in the table below.

Term Node-based definition Character-based definition
Monophyly A clade, a monophyletic taxon, is a taxon that includes all descendants of an inferred ancestor. A clade is characterized by one or more apomorphies: derived character states present in the first member of the taxon, inherited by its descendants (unless secondarily lost), and not inherited by any other taxa.
Paraphyly A paraphyletic assemblage is one that is constructed by taking a clade and removing one or more smaller clades.[27] (Removing one clade produces a singly paraphyletic assemblage, removing two produces a doubly paraphylectic assemblage, and so on.)[28] A paraphyletic assemblage is characterized by one or more plesiomorphies: character states inherited from ancestors but not present in all of their descendants. As a consequence, a paraphyletic assemblage is truncated, in that it excludes one or more clades from an otherwise monophyletic taxon. An alternative name is evolutionary grade, referring to an ancestral character state within the group. While paraphyletic assemblages are popular among paleontologists and evolutionary taxonomists, cladists do not recognize paraphyletic assemblages as having any formal information content – they are merely parts of clades.
Polyphyly A polyphyletic assemblage is one which is neither monophyletic nor paraphyletic. A polyphyletic assemblage is characterized by one or more homoplasies: character states which have converged or reverted so as to be the same but which have not been inherited from a common ancestor. No systematist recognizes polyphyletic assemblages as taxonomically meaningful entities, although ecologists sometimes consider them meaningful labels for functional participants in ecological communities (e. g., primary producers, detritivores, etc.).

Criticism

Cladistics, either generally or in specific applications, has been criticized from its beginnings. Decisions as to whether particular character states are homologous, a precondition of their being synapomorphies, have been challenged as involving circular reasoning and subjective judgements.[29]
Transformed cladistics arose in the late 1970s in an attempt to resolve some of these problems by removing phylogeny from cladistic analysis, but it has remained unpopular.

However, homology is usually determined from analysis of the results that are evaluated with homology measures, mainly the CI (consistency index) and RI (retention index), which, it has been claimed,[by whom?] makes the process objective. Also, homology can be equated to synapomorphy, which is what Patterson has done.[30]

In disciplines other than biology

The comparisons used to acquire data on which cladograms can be based are not limited to the field of biology.[31] Any group of individuals or classes that are hypothesized to have a common ancestor, and to which a set of common characteristics may or may not apply, can be compared pairwise. Cladograms can be used to depict the hypothetical descent relationships within groups of items in many different academic realms. The only requirement is that the items have characteristics that can be identified and measured.

Anthropology and archaeology:[32] Cladistic methods have been used to reconstruct the development of cultures or artifacts using groups of cultural traits or artifact features.

Comparative mythology and folktale use cladistic methods to reconstruct the protoversion of many myths. Mythological phylogenies constructed with mythemes clearly support low horizontal transmissions (borrowings), historical (sometimes Palaeolithic) diffusions and punctuated evolution.[33] They also are a powerful way to test hypotheses about cross-cultural relationships among folktales.[34][35]

Literature: Cladistic methods have been used in the classification of the surviving manuscripts of the Canterbury Tales,[36] and the manuscripts of the Sanskrit Charaka Samhita.[37]

Historical linguistics:[38] Cladistic methods have been used to reconstruct the phylogeny of languages using linguistic features. This is similar to the traditional comparative method of historical linguistics, but is more explicit in its use of parsimony and allows much faster analysis of large datasets (computational phylogenetics).

Textual criticism or stemmatics:[37][39] Cladistic methods have been used to reconstruct the phylogeny of manuscripts of the same work (and reconstruct the lost original) using distinctive copying errors as apomorphies. This differs from traditional historical-comparative linguistics in enabling the editor to evaluate and place in genetic relationship large groups of manuscripts with large numbers of variants that would be impossible to handle manually. It also enables parsimony analysis of contaminated traditions of transmission that would be impossible to evaluate manually in a reasonable period of time.

Astrophysics[40] infers the history of relationships between galaxies to create branching diagram hypotheses of galaxy diversification.

Effective population size

From Wikipedia, the free encyclopedia

The effective population size is "the number of individuals in a population who contribute offspring to the next generation," or all the breeding adults in that population. Genetically derived estimates of effective population size tend to provide a lower number than an actual head count would provide.[1] In more technical terms, the effective population size is the number of individuals that an idealised population would need to have, in order for some specified quantity of interest to be the same in the idealised population as in the real population. Idealised populations are based on unrealistic but convenient simplifications such as random mating, simultaneous birth of each new generation, constant population size, and equal numbers of children per parent. In some simple scenarios, the effective population size is the number of breeding individuals in the population. However, for most quantities of interest and most real populations, the census population size N of a real population is usually larger than the effective population size Ne. The same population may have multiple effective population sizes, for different properties of interest, including for different genetic loci.

The effective population size is most commonly measured with respect to the coalescence time. In an idealised diploid population with no selection at any locus, the expectation of the coalescence time in generations is equal to twice the census population size. The effective population size is measured as within-species genetic diversity divided by four times the mutation rate, because in such an idealised population, the heterozygosity is equal to {\displaystyle 4N\mu }. In a population with selection at many loci and abundant linkage disequilibrium, the coalescent effective population size may not reflect the census population size at all, or may reflect its logarithm.

The concept of effective population size was introduced in the field of population genetics in 1931 by the American geneticist Sewall Wright.[2][3]

Overview: Types of effective population size

Depending on the quantity of interest, effective population size can be defined in several ways. Ronald Fisher and Sewall Wright originally defined it as "the number of breeding individuals in an idealised population that would show the same amount of dispersion of allele frequencies under random genetic drift or the same amount of inbreeding as the population under consideration". More generally, an effective population size may be defined as the number of individuals in an idealised population that has a value of any given population genetic quantity that is equal to the value of that quantity in the population of interest. The two population genetic quantities identified by Wright were the one-generation increase in variance across replicate populations (variance effective population size) and the one-generation change in the inbreeding coefficient (inbreeding effective population size). These two are closely linked, and derived from F-statistics, but they are not identical.[4]

Today, the effective population size is usually estimated empirically with respect to the sojourn or coalescence time, estimated as the within-species genetic diversity divided by the mutation rate, yielding a coalescent effective population size.[5] Another important effective population size is the selection effective population size 1/scritical, where scritical is the critical value of the selection coefficient at which selection becomes more important than genetic drift.[6]

Empirical measurements

In Drosophila populations of census size 16, the variance effective population size has been measured as equal to 11.5.[7] This measurement was achieved through studying changes in the frequency of a neutral allele from one generation to another in over 100 replicate populations.

For coalescent effective population sizes, a survey of publications on 102 mostly wildlife animal and plant species yielded 192 Ne/N ratios. Seven different estimation methods were used in the surveyed studies. Accordingly, the ratios ranged widely from 10-6 for Pacific oysters to 0.994 for humans, with an average of 0.34 across the examined species.[8] A genealogical analysis of human hunter-gatherers (Eskimos) determined the effective-to-census population size ratio for haploid (mitochondrial DNA, Y chromosomal DNA), and diploid (autosomal DNA) loci separately: the ratio of the effective to the census population size was estimated as 0.6–0.7 for autosomal and X-chromosomal DNA, 0.7–0.9 for mitochondrial DNA and 0.5 for Y-chromosomal DNA.[9]

Variance effective size

References missing In the Wright-Fisher idealized population model, the conditional variance of the allele frequency p', given the allele frequency p in the previous generation, is
\operatorname {var}(p'\mid p)={p(1-p) \over 2N}.
Let \widehat {\operatorname {var}}(p'\mid p) denote the same, typically larger, variance in the actual population under consideration. The variance effective population size N_{e}^{{(v)}} is defined as the size of an idealized population with the same variance. This is found by substituting \widehat {\operatorname {var}}(p'\mid p) for \operatorname {var}(p'\mid p) and solving for N which gives
N_{e}^{{(v)}}={p(1-p) \over 2\widehat {\operatorname {var}}(p)}.

Theoretical examples

In the following examples, one or more of the assumptions of a strictly idealised population are relaxed, while other assumptions are retained. The variance effective population size of the more relaxed population model is then calculated with respect to the strict model.

Variations in population size

Population size varies over time. Suppose there are t non-overlapping generations, then effective population size is given by the harmonic mean of the population sizes[10]:
{1 \over N_{e}}={1 \over t}\sum _{{i=1}}^{t}{1 \over N_{i}}
For example, say the population size was N = 10, 100, 50, 80, 20, 500 for six generations (t = 6). Then the effective population size is the harmonic mean of these, giving:
{1 \over N_{e}} ={{\begin{matrix}{\frac  {1}{10}}\end{matrix}}+{\begin{matrix}{\frac  {1}{100}}\end{matrix}}+{\begin{matrix}{\frac  {1}{50}}\end{matrix}}+{\begin{matrix}{\frac  {1}{80}}\end{matrix}}+{\begin{matrix}{\frac  {1}{20}}\end{matrix}}+{\begin{matrix}{\frac  {1}{500}}\end{matrix}} \over 6}

={0.1945 \over 6}

=0.032416667
N_e =30.8
Note this is less than the arithmetic mean of the population size, which in this example is 126.7. The harmonic mean tends to be dominated by the smallest bottleneck that the population goes through.

Dioeciousness

If a population is dioecious, i.e. there is no self-fertilisation then
N_{e}=N+{\begin{matrix}{\frac  {1}{2}}\end{matrix}}
or more generally,
N_{e}=N+{\begin{matrix}{\frac  {D}{2}}\end{matrix}}
where D represents dioeciousness and may take the value 0 (for not dioecious) or 1 for dioecious.

When N is large, Ne approximately equals N, so this is usually trivial and often ignored:
N_{e}=N+{\begin{matrix}{\frac  {1}{2}}\approx N\end{matrix}}

Variance in reproductive success

If population size is to remain constant, each individual must contribute on average two gametes to the next generation. An idealized population assumes that this follows a Poisson distribution so that the variance of the number of gametes contributed, k is equal to the mean number contributed, i.e. 2:
\operatorname {var}(k)={\bar  {k}}=2.
However, in natural populations the variance is often larger than this. The vast majority of individuals may have no offspring, and the next generation stems only from a small number of individuals, so
\operatorname {var}(k)>2.
The effective population size is then smaller, and given by:
N_{e}^{{(v)}}={4N-2D \over 2+\operatorname {var}(k)}
Note that if the variance of k is less than 2, Ne is greater than N. In the extreme case of a population experiencing no variation in family size, in a laboratory population in which the number of offspring is artificially controlled, Vk = 0 and Ne = 2N.

Non-Fisherian sex-ratios

When the sex ratio of a population varies from the Fisherian 1:1 ratio, effective population size is given by:
N_{e}^{{(v)}}=N_{e}^{{(F)}}={4N_{m}N_{f} \over N_{m}+N_{f}}
Where Nm is the number of males and Nf the number of females. For example, with 80 males and 20 females (an absolute population size of 100):
N_e ={4\times 80\times 20 \over 80+20}

={6400 \over 100}

=64
Again, this results in Ne being less than N.

Inbreeding effective size

Alternatively, the effective population size may be defined by noting how the average inbreeding coefficient changes from one generation to the next, and then defining Ne as the size of the idealized population that has the same change in average inbreeding coefficient as the population under consideration. The presentation follows Kempthorne (1957).[11]

For the idealized population, the inbreeding coefficients follow the recurrence equation
F_{t}={\frac  {1}{N}}\left({\frac  {1+F_{{t-2}}}{2}}\right)+\left(1-{\frac  {1}{N}}\right)F_{{t-1}}.
Using Panmictic Index (1 − F) instead of inbreeding coefficient, we get the approximate recurrence equation
1-F_{t}=P_{t}=P_{0}\left(1-{\frac  {1}{2N}}\right)^{t}.
The difference per generation is
{\frac  {P_{{t+1}}}{P_{t}}}=1-{\frac  {1}{2N}}.
The inbreeding effective size can be found by solving
{\frac  {P_{{t+1}}}{P_{t}}}=1-{\frac  {1}{2N_{e}^{{(F)}}}}.
This is
N_{e}^{{(F)}}={\frac  {1}{2\left(1-{\frac  {P_{{t+1}}}{P_{t}}}\right)}}
although researchers rarely use this equation directly.

Theoretical example: overlapping generations and age-structured populations

When organisms live longer than one breeding season, effective population sizes have to take into account the life tables for the species.

Haploid

Assume a haploid population with discrete age structure. An example might be an organism that can survive several discrete breeding seasons. Further, define the following age structure characteristics:
v_{i}= Fisher's reproductive value for age i,
\ell _{i}= The chance an individual will survive to age i, and
N_{0}= The number of newborn individuals per breeding season.
The generation time is calculated as
T=\sum _{{i=0}}^{\infty }\ell _{i}v_{i}= average age of a reproducing individual
Then, the inbreeding effective population size is[12]
N_{e}^{{(F)}}={\frac  {N_{0}T}{1+\sum _{i}\ell _{{i+1}}^{2}v_{{i+1}}^{2}({\frac  {1}{\ell _{{i+1}}}}-{\frac  {1}{\ell _{i}}})}}.

Diploid

Similarly, the inbreeding effective number can be calculated for a diploid population with discrete age structure. This was first given by Johnson,[13] but the notation more closely resembles Emigh and Pollak.[14]

Assume the same basic parameters for the life table as given for the haploid case, but distinguishing between male and female, such as N0ƒ and N0m for the number of newborn females and males, respectively (notice lower case ƒ for females, compared to upper case F for inbreeding).

The inbreeding effective number is
{\begin{aligned}{\frac  {1}{N_{e}^{{(F)}}}}={\frac  {1}{4T}}\left\{{\frac  {1}{N_{0}^{f}}}+{\frac  {1}{N_{0}^{m}}}+\sum _{i}\left(\ell _{{i+1}}^{f}\right)^{2}\left(v_{{i+1}}^{f}\right)^{2}\left({\frac  {1}{\ell _{{i+1}}^{f}}}-{\frac  {1}{\ell _{i}^{f}}}\right)\right.\,\,\,\,\,\,\,\,&\\\left.{}+\sum _{i}\left(\ell _{{i+1}}^{m}\right)^{2}\left(v_{{i+1}}^{m}\right)^{2}\left({\frac  {1}{\ell _{{i+1}}^{m}}}-{\frac  {1}{\ell _{i}^{m}}}\right)\right\}.&\end{aligned}}

  

 

Coalescent effective size

According to the neutral theory of molecular evolution, a neutral allele remains in a population for Ne generations, where Ne is the effective population size. An idealised diploid population will have a pairwise nucleotide diversity equal to 4\mu Ne, where \mu is the mutation rate. The sojourn effective population size can therefore be estimated empirically by dividing the nucleotide diversity by the mutation rate.[5]

The coalescent effective size may have little relationship to the number of individuals physically present in a population.[15] Measured coalescent effective population sizes vary between genes in the same population, being low in genome areas of low recombination and high in genome areas of high recombination.[16][17] Sojourn times are proportional to N in neutral theory, but for alleles under selection, sojourn times are proportional to log(N). Genetic hitchhiking can cause neutral mutations to have sojourn times proportional to log(N): this may explain the relationship between measured effective population size and the local recombination rate.

Selection effective size

In an idealised Wright-Fisher model, the fate of an allele, beginning at an intermediate frequency, is largely determined by selection if the selection coefficient s ≫ 1/N, and largely determined by neutral genetic drift if s ≪ 1/N. In real populations, the cutoff value of s may depend instead on local recombination rates.[6][18] This limit to selection in a real population may be captured in a toy Wright-Fisher simulation through the appropriate choice of Ne. Populations with different selection effective population sizes are predicted to evolve profoundly different genome architectures.[19][20]

Psychological nativism

From Wikipedia, the free encyclopedia   In the field of psychology , nativis...