Statistics Calculation

class Stat

class Stat

Operator Stat calculates various statistics of the population being applied and sets variables in its local namespace. Other operators or functions can retrieve results from or evalulate expressions in this local namespace after Stat is applied.

Stat(popSize=False, numOfMales=False, numOfAffected=False, numOfSegSites=[], numOfMutants=[], alleleFreq=[], heteroFreq=[], homoFreq=[], genoFreq=[], haploFreq=[], haploHeteroFreq=[], haploHomoFreq=[], sumOfInfo=[], meanOfInfo=[], varOfInfo=[], maxOfInfo=[], minOfInfo=[], LD=[], association=[], neutrality=[], structure=[], HWE=[], inbreeding=[], effectiveSize=[], vars=ALL_AVAIL, suffix="", output="", begin=0, end=-1, step=1, at=[], reps=ALL_AVAIL, subPops=ALL_AVAIL, infoFields=[])

Create a Stat operator that calculates specified statistics of a population when it is applied to this population. This operator can be applied to specified replicates (parameter rep) at specified generations (parameter begin, end, step, and at). This operator does not produce any output (ignore parameter output) after statistics are calculated. Instead, it stores results in the local namespace of the population being applied. Other operators can retrieve these variables or evalulate expression directly in this local namespace. Please refer to operator BaseOperator for a detailed explanation of these common operator parameters.

Stat supports parameter subPops. It usually calculate the same set of statistics for all subpopulations (subPops=subPopList()). If a list of (virtual) subpopulations are specified, statistics for only specified subpopulations will be calculated. However, different statistics treat this parameter differently and it is very important to check its reference before you use subPops for any statistics.

Calculated statistics are saved as variables in a population’s local namespace. These variables can be numbers, lists or dictionaries and can be retrieved using functions Population.vars() or Population.dvars(). A special default dictionary (defdict) is used for dictionaries whose keys are determined dynamically. Accessing elements of such a dictionary with an invalid key will yield value 0 instead of a KeyError. If the same variables are calculated for one or more (virtual) subpopulation, the variables are stored in vars()['subPop'][sp]['var'] where sp is a subpopulation ID (sp) or a tuple of virtual subpopulation ID ((sp, vsp)). Population.vars(sp) and Population.dvars(sp) provide shortcuts to these variables.

Operator Stat outputs a number of most useful variables for each type of statistic. For example, alleleFreq calculates both allele counts and allele frequencies and it by default sets variable alleleFreq (dvars().alleleFreq) for all or specified subpopulations. If this does not fit your need, you can use parameter vars to output additional parameters, or limit the output of existing parameters. More specifically, for this particular statistic, the available variables are 'alleleFreq', 'alleleNum', 'alleleFreq_sp' ('alleleFreq' in each subpopulation), and 'alleleNum_sp' ('alleleNum' in each subpopulation). You can set vars=['alleleNum_sp'] to output only subpopulation specific allele count. An optional suffix (parameter suffix) can be used to append a suffix to default parameter names. This parameter can be used, for example, to calculate and store the same statistics for different subpopulations (e.g. pairwise Fst).

Operator Stat supports the following statistics:

popSize: If popSize=True, number of individuals in all or specified subpopulations (parameter subPops) will be set to the following variables:

  • popSize (default): Number of individuals in all or specified subpopulations. Because subPops does not have to cover all individuals, it may not be the actual population size.
  • popSize_sp: Size of (virtual) subpopulation sp.
  • subPopSize (default): A list of (virtual) subpopulation sizes. This variable is easier to use than accessing popSize from each (virtual) subpopulation.

numOfMales: If numOfMales=True, number of male individuals in all or specified (virtual) subpopulations will be set to the following variables:

  • numOfMales (default): Total number of male individuals in all or specified (virtual) subpopulations.
  • numOfFemales (default): Total number of female individuals in all or specified (virtual) subpopulations.
  • propOfMales: Proportion of male individuals.
  • propOfFemales: Proportion of female individuals.
  • numOfMales_sp: Number of male individuals in each (virtual) subpopulation.
  • numOfFemales_sp: Number of female individuals in each (virtual) subpopulation.
  • propOfMales_sp: Proportion of male individuals in each (virtual) subpopulation.
  • propOfFemales_sp: Proportion of female individuals in each (virtual) subpopulation.

numOfAffected: If numOfAffected=True, number of affected individuals in all or specified (virtual) subpopulations will be set to the following variables:

  • numOfAffected (default): Total number of affected individuals in all or specified (virtual) subpopulations.
  • numOfUnaffected (default): Total number of unaffected individuals in all or specified (virtual) subpopulations.
  • propOfAffected: Proportion of affected individuals.
  • propOfUnaffected: Proportion of unaffected individuals.
  • numOfAffected_sp: Number of affected individuals in each (virtual) subpopulation.
  • numOfUnaffected_sp: Number of unaffected individuals in each (virtual) subpopulation.
  • propOfAffected_sp: Proportion of affected individuals in each (virtual) subpopulation.
  • propOfUnaffected_sp: Proportion of unaffected individuals in each (virtual) subpopulation.

numOfSegSites: Parameter numOfSegSites accepts a list of loci (loci indexes, names, or ALL_AVAIL) and count the number of loci with at least two different alleles (segregating sites) or loci with only one non-zero allele (no zero allele, not segragating) for individuals in all or specified (virtual) subpopulations. This parameter sets variables

  • numOfSegSites (default): Number of segregating sites in all or specified (virtual) subpopulations.
  • numOfSegSites_sp: Number of segregating sites in each (virtual) subpopulation.
  • numOfFixedSites: Number of sites with one non-zero allele in all or specified (virtual) subpopulations.
  • numOfFixedSites_sp: Number of sites with one non-zero allele in in each (virtual) subpopulations.
  • segSites: A list of segregating sites in all or specified (virtual) subpopulations.
  • segSites_sp: A list of segregating sites in each (virtual) subpopulation.
  • fixedSites: A list of sites with one non-zero allele in all or specified (virtual) subpopulations.
  • fixedSites_sp: A list of sites with one non-zero allele in in each (virtual) subpopulations.

numOfMutants: Parameter numOfMutants accepts a list of loci (loci indexes, names, or ALL_AVAIL) and count the number of mutants (non-zero alleles) for individuals in all or specified (virtual) subpopulations. It sets variables

  • numOfMutants (default): Number of mutants in all or specified (virtual) subpopulations.
  • numOfMutants_sp: Number of mutants in each (virtual) subpopulations.

alleleFreq: This parameter accepts a list of loci (loci indexes, names, or ALL_AVAIL), at which allele frequencies will be calculated. This statistic outputs the following variables, all of which are dictionary (with loci indexes as keys) of default dictionaries (with alleles as keys). For example, alleleFreq[loc][a] returns 0 if allele a does not exist.

  • alleleFreq (default): alleleFreq[loc][a] is the frequency of allele a at locus loc for all or specified (virtual) subpopulations.
  • alleleNum (default): alleleNum[loc][a] is the number of allele a at locus loc for all or specified (virtual) subpopulations.
  • alleleFreq_sp: Allele frequency in each (virtual) subpopulation.
  • alleleNum_sp: Allele count in each (virtual) subpopulation.

heteroFreq and homoFreq: These parameters accept a list of loci (by indexes or names), at which the number and frequency of homozygotes and/or heterozygotes will be calculated. These statistics are only available for diploid populations. The following variables will be outputted:

  • heteroFreq (default for parameter heteroFreq): A dictionary of proportion of heterozygotes in all or specified (virtual) subpopulations, with loci indexes as dictionary keys.
  • homoFreq (default for parameter homoFreq): A dictionary of proportion of homozygotes in all or specified (virtual) subpopulations.
  • heteroNum: A dictionary of number of heterozygotes in all or specified (virtual) subpopulations.
  • homoNum: A dictionary of number of homozygotes in all or specified (virtual) subpopulations.
  • heteroFreq_sp: A dictionary of proportion of heterozygotes in each (virtual) subpopulation.
  • homoFreq_sp: A dictionary of proportion of homozygotes in each (virtual) subpopulation.
  • heteroNum_sp: A dictionary of number of heterozygotes in each (virtual) subpopulation.
  • homoNum_sp: A dictionary of number of homozygotes in each (virtual) subpopulation.

genoFreq: This parameter accept a list of loci (by indexes or names) at which number and frequency of all genotypes are outputed as a dictionary (indexed by loci indexes) of default dictionaries (indexed by tuples of possible indexes). This statistic is available for all population types with genotype defined as ordered alleles at a locus. The length of genotype equals the number of homologous copies of chromosomes (ploidy) of a population. Genotypes for males or females on sex chromosomes or in haplodiploid populations will have different length. Because genotypes are ordered, (1, 0) and (0, 1) (two possible genotypes in a diploid population) are considered as different genotypes. This statistic outputs the following variables:

  • genoFreq (default): A dictionary (by loci indexes) of default dictionaries (by genotype) of genotype frequencies. For example, genoFreq[1][(1, 0)] is the frequency of genotype (1, 0) at locus 1.
  • genoNum (default): A dictionary of default dictionaries of genotype counts of all or specified (virtual) subpopulations.
  • genoFreq_sp: genotype frequency in each specified (virtual) subpopulation.
  • genoFreq_sp: genotype count in each specified (virtual) subpopulation.

haploFreq: This parameter accepts one or more lists of loci (by index) at which number and frequency of haplotypes are outputted as default dictionaries. [(1,2)] can be abbreviated to (1,2). For example, using parameter haploFreq=(1,2,4), all haplotypes at loci 1, 2 and 4 are counted. This statistic saves results to dictionary (with loci index as keys) of default dictionaries (with haplotypes as keys) such as haploFreq[(1,2,4)][(1,1,0)] (frequency of haplotype (1,1,0) at loci (1,2,3)). This statistic works for all population types. Number of haplotypes for each individual equals to his/her ploidy number. Haplodiploid populations are supported in the sense that the second homologous copy of the haplotype is not counted for male individuals. This statistic outputs the following variables:

  • haploFreq (default): A dictionary (with tuples of loci indexes as keys) of default dictionaries of haplotype frequencies. For example, haploFreq[(0, 1)][(1,1)] records the frequency of haplotype (1,1) at loci (0, 1) in all or specified (virtual) subpopulations.
  • haploNum (default): A dictionary of default dictionaries of haplotype counts in all or specified (virtual) subpopulations.
  • haploFreq_sp: Halptype frequencies in each (virtual) subpopulation.
  • haploNum_sp: Halptype count in each (virtual) subpopulation.

haploHeteroFreq and haploHomoFreq: These parameters accept a list of haplotypes (list of loci), at which the number and frequency of haplotype homozygotes and/or heterozygotes will be calculated. Note that these statistics are observed count of haplotype heterozygote. The following variables will be outputted:

  • haploHeteroFreq (default for parameter haploHeteroFreq): A dictionary of proportion of haplotype heterozygotes in all or specified (virtual) subpopulations, with haplotype indexes as dictionary keys.
  • haploHomoFreq (default for parameter haploHomoFreq): A dictionary of proportion of homozygotes in all or specified (virtual) subpopulations.
  • haploHeteroNum: A dictionary of number of heterozygotes in all or specified (virtual) subpopulations.
  • haploHomoNum: A dictionary of number of homozygotes in all or specified (virtual) subpopulations.
  • haploHeteroFreq_sp: A dictionary of proportion of heterozygotes in each (virtual) subpopulation.
  • haploHomoFreq_sp: A dictionary of proportion of homozygotes in each (virtual) subpopulation.
  • haploHeteroNum_sp: A dictionary of number of heterozygotes in each (virtual) subpopulation.
  • haploHomoNum_sp: A dictionary of number of homozygotes in each (virtual) subpopulation.

sumOfinfo, meanOfInfo, varOfInfo, maxOfInfo and minOfInfo: Each of these five parameters accepts a list of information fields. For each information field, the sum, mean, variance, maximum or minimal (depending on the specified parameter(s)) of this information field at iddividuals in all or specified (virtual) subpopulations will be calculated. The results will be put into the following population variables:

  • sumOfInfo (default for sumOfInfo): A dictionary of the sum of specified information fields of individuals in all or specified (virtual) subpopulations. This dictionary is indexed by names of information fields.
  • meanOfInfo (default for meanOfInfo): A dictionary of the mean of information fields of all individuals.
  • varOfInfo (default for varOfInfo): A dictionary of the sample variance of information fields of all individuals.
  • maxOfInfo (default for maxOfInfo): A dictionary of the maximum value of information fields of all individuals.
  • minOfInfo (default for minOfInfo): A dictionary of the minimal value of information fields of all individuals.
  • sumOfInfo_sp: A dictionary of the sum of information fields of individuals in each subpopulation.
  • meanOfInfo_sp: A dictionary of the mean of information fields of individuals in each subpopulation.
  • varOfInfo_sp: A dictionary of the sample variance of information fields of individuals in each subpopulation.
  • maxOfInfo_sp: A dictionary of the maximum value of information fields of individuals in each subpopulation.
  • minOfInfo_sp: A dictionary of the minimal value of information fields of individuals in each subpopulation.

LD: Parameter LD accepts one or a list of loci pairs (e.g. LD=[[0,1], [2,3]]) with optional primary alleles at both loci (e.g. LD=[0,1,0,0]). For each pair of loci, this operator calculates linkage disequilibrium and optional association statistics between two loci. When primary alleles are specified, signed linkage disequilibrium values are calculated with non-primary alleles are combined. Otherwise, absolute values of diallelic measures are combined to yield positive measure of LD. Association measures are calculated from a m by n contigency of haplotype counts (m=n=2 if primary alleles are specified). Please refer to the simuPOP user’s guide for detailed information. This statistic sets the following variables:

  • LD (default) Basic LD measure for haplotypes in all or specified (virtual) subpopulations. Signed if primary alleles are specified.
  • LD_prime (default) Lewontin’s D’ measure for haplotypes in all or specified (virtual) subpopulations. Signed if primary alleles are specified.
  • R2 (default) Correlation LD measure for haplotypes in all or specified (virtual) subpopulations.
  • LD_ChiSq ChiSq statistics for a contigency table with frequencies of haplotypes in all or specified (virtual) subpopulations.
  • LD_ChiSq_p Single side p-value for the ChiSq statistic. Degrees of freedom is determined by number of alleles at both loci and the specification of primary alleles.
  • CramerV Normalized ChiSq statistics.
  • LD_sp Basic LD measure for haplotypes in each (virtual) subpopulation.
  • LD_prime_sp Lewontin’s D’ measure for haplotypes in each (virtual) subpopulation.
  • R2_sp R2 measure for haplotypes in each (virtual) subpopulation.
  • LD_ChiSq_sp ChiSq statistics for each (virtual) subpopulation.
  • LD_ChiSq_p_sp p value for the ChiSq statistics for each (virtual) subpopulation.
  • CramerV_sp Cramer V statistics for each (virtual) subpopulation.

association: Parameter association accepts a list of loci, which can be a list of indexes, names, or ALL_AVAIL. At each locus, one or more statistical tests will be performed to test association between this locus and individual affection status. Currently, simuPOP provides the following tests:

  • An allele-based Chi-square test using alleles counts. This test can be applied to loci with more than two alleles, and to haploid populations.
  • A genotype-based Chi-square test using genotype counts. This test can be applied to loci with more than two alleles (more than 3 genotypes) in diploid populations. aA and Aa are considered to be the same genotype.
  • A genotype-based Cochran-Armitage trend test. This test can only be applied to diallelic loci in diploid populations. A codominant model is assumed.

This statistic sets the following variables:

  • Allele_ChiSq A dictionary of allele-based Chi-Square statistics for each locus, using cases and controls in all or specified (virtual) subpopulations.
  • Allele_ChiSq_p (default) A dictionary of p-values of the corresponding Chi-square statistics.
  • Geno_ChiSq A dictionary of genotype-based Chi-Square statistics for each locus, using cases and controls in all or specified (virtual) subpopulations.
  • Geno_ChiSq_p A dictionary of p-values of the corresponding genotype-based Chi-square test.
  • Armitage_p A dictionary of p-values of the Cochran- Armitage tests, using cases and controls in all or specified (virtual) subpopulations.
  • Allele_ChiSq_sp A dictionary of allele-based Chi-Square statistics for each locus, using cases and controls from each subpopulation.
  • Allele_ChiSq_p_sp A dictionary of p-values of allele-based Chi-square tests, using cases and controls from each (virtual) subpopulation.
  • Geno_ChiSq_sp A dictionary of genotype-based Chi-Square tests for each locus, using cases and controls from each subpopulation.
  • Geno_ChiSq_p_sp A dictionary of p-values of genotype-based Chi-Square tests, using cases and controls from each subpopulation.
  • Armitage_p_sp A dictionary of p-values of the Cochran- Armitage tests, using cases and controls from each subpopulation.

neutrality: This parameter performs neutrality tests (detection of natural selection) on specified loci, which can be a list of loci indexes, names or ALL_AVAIL. It currently only outputs Pi, which is the average number of pairwise difference between loci. This statistic outputs the following variables:

  • Pi Mean pairwise difference between all sequences from all or specified (virtual) subpopulations.
  • Pi_sp Mean paiewise difference between all sequences in each (virtual) subpopulation.

structure: Parameter structure accepts a list of loci at which statistics that measure population structure are calculated. structure accepts a list of loci indexes, names or ALL_AVAIL. This parameter currently supports the following statistics:

  • Weir and Cockerham’s Fst (1984). This is the most widely used estimator of Wright’s fixation index and can be used to measure Population differentiation. However, this method is designed to estimate Fst from samples of larger populations and might not be appropriate for the calculation of Fst of large populations.
  • Nei’s Gst (1973). The Gst estimator is another estimator for Wright’s fixation index but it is extended for multi-allele (more than two alleles) and multi-loci cases. This statistics should be used if you would like to obtain a true Fst value of a large Population. Nei’s Gst uses only allele frequency information so it is available for all population type (haploid, diploid etc). Weir and Cockerham’s Fst uses heterozygosity frequency so it is best for autosome of diploid populations. For non-diploid population, sex, and mitochondrial DNAs, simuPOP uses expected heterozygosity (1 - sum p_i^2) when heterozygosity is needed. These statistics output the following variables:
  • F_st (default) The WC84 Fst statistic estimated for all * specified loci.
  • F_is The WC84 Fis statistic estimated for all specified loci.
  • F_it The WC84 Fit statistic estimated for all specified loci.
  • f_st A dictionary of locus level WC84 Fst values.
  • f_is A dictionary of locus level WC84 Fis values.
  • f_it A dictionary of locus level WC84 Fit values.
  • G_st Nei’s Gst statistic estimated for all specified loci.
  • g_st A dictionary of Nei’s Gst statistic estimated for each locus.

HWE: Parameter HWE accepts a list of loci at which exact two-side tests for Hardy-Weinberg equilibrium will be performed. This statistic is only available for diallelic loci in diploid populations. HWE can be a list of loci indexes, names or ALL_AVAIL. This statistic outputs the following variables:

  • HWE (default) A dictionary of p-values of HWE tests using genotypes in all or specified (virtual) subpopulations.
  • HWE_sp A dictionary of p-values of HWS tests using genotypes in each (virtual) subpopulation.

inbreeding: Inbreeding measured by Identitcal by Decent (and by State). This statistics go through all loci of individuals in a diploid population and calculate the number and proportions of alleles that are identitcal by decent and by state. Because ancestral information is only available in lineage module, variables IBD_freq are always set to zero in other modules. Loci on sex and mitochondrial chromosomes, and non-diploid populations are currently not supported. This statistic outputs the following variables:

  • IBD_freq (default) The frequency of IBD pairs among all allele pairs. To use this statistic, the population must be initialized by operator InitLineage() to assign each ancestral allele an unique identify.
  • IBS_freq (default) The proportion of IBS pairs among all allele pairs.
  • IBD_freq_sp frequency of IBD in each (virtual) subpopulations.
  • IBS_freq_sp frequency of IBS in each (virtual) subpopulations.

effectiveSize: Parameter effectiveSize accepts a list of loci at which the effective population size for the whole or specified (virtual) subpopulations is calculated. effectiveSize can be a list of loci indexes, names or ALL_AVAIL. Parameter subPops is usually used to define samples from which effective sizes are estimated. This statistic allows the calculation of true effective size based on number of gametes each parents transmit to the offspring population (per- locus before and after mating), and estimated effective size based on sample genotypes. Due to the temporal natural of some methods, more than one Stat operators might be needed to calculate effective size. The vars parameter specified which method to use and which variable to set. Acceptable values include:

  • Ne_demo_base When this variable is set before mating, it stores IDs of breeding parents and, more importantly, assign an unique lineage value to alleles at specified loci of each individual. This feature is only available for lineage modules and will change lineage values at specified loci of all individuals.
  • Ne_demo_base_sp Pre-mating information for each (virtual) subpopulation, used by variable Ne_demo_sp.
  • Ne_demo A dictionary of locus-specific demographic effective population size, calculated using number of gemetes each parent transmits to the offspring population. The method is vased on Crow & Denniston 1988 (Ne = KN-1/k-1+Vk/k) and need variable Ne_demo_base set before mating. Effective size estimated from this formula is model dependent and might not be applicable to your mating schemes.
  • Ne_demo_sp Calculate subpopulation-specific effective size.
  • Ne_temporal_base When this variable is set in parameter vars, the Stat operator saves baseline allele frequencies and other information in this variable, which are used by temporary methods to estimate effective population size according to changes in allele frequency between the baseline and present generations. This variable could be set repeatedly to change baselines.
  • Ne_temporal_base_sp Set baseline information for each (virtual) subpopulation specified.
  • Ne_tempoFS_P1 Effective population size, 2.5% and 97.5% confidence interval for sampling plan 1 as a list of size 3, estimated using a temporal method as described in Jorde & Ryman (2007), and as implemented by software tempoFS (http://www.zoologi.su.se/~ryman/). This variable is set to census population size if no baseline has been set, and to the temporal effective size between the present and the baseline generation otherwise. This method uses population size or sum of subpopulation sizes of specified (virtual) subpopulations as census population size for the calculation based on plan 1.
  • Ne_tempoFS_P2 Effective population size, 2.5% and 97.5% confidence interval for sampling plan 2 as a list of size 6, estimated using a temporal method as described in Jorde & Ryman (2007). This variable is set to census population size no baseline has been set, and to the temporal effective size between the present and the baseline generation otherwise. This method assumes that the sample is drawn from an infinitely-sized population.
  • Ne_tempoFS deprecated, use Ne_tempoFS_P2 instead.
  • Ne_tempoFS_P1_sp Estimate effective size of each (virtual) subpopulation using method Jorde & Ryman 2007, assuming sampling plan 1. The census population sizes for sampling plan 1 are the sizes for each subpopulation that contain the specified (virtual) subpopulations.
  • Ne_tempoFS_P2_sp Estimate effective size of each (virtual) subpopulation using method Jorde & Ryman 2007, assuming sampling plan 2.
  • Ne_tempoFS_sp deprecated, use Ne_tempoFS_P2_sp instead.
  • Ne_waples89_P1 Effective population size, 2.5% and 97.5% confidence interval for sampling plan 1 as a list of size 6, estimated using a temporal method as described in Waples 1989, Genetics. Because this is a temporal method, Ne_waples89 estimates effective size between the present and the baseline generation set by variable Ne_temporal_base. Census population size will be resutned if no baseline has been set. This method uses population size or sum of subpopulation sizes of specified (virtual) subpopulations as census population size for the calculation based on plan 1.
  • Ne_waples89_P2 Effective population size, 2.5% and 97.5% confidence interval for sampling plan 2 as a list of size 6, estimated using a temporal method as described in Waples 1989, Genetics. Because this is a temporal method, Ne_waples89 estimates effective size between the present and the baseline generation set by variable Ne_temporal_base. Census population size will be returned if no baseline has been set.
  • Ne_waples89_P1_sp Estimate effective size for each (virtual) subpopulation using method Waples 89, assuming sampling plan 1. The census population sizes are the sizes for each subpopulation that contain the specified (virtual) subpopulation.
  • Ne_waples89_P2_sp Estimate effective size for each (virtual) subpopulation using method Waples 89, assuming sampling plan 2.
  • Ne_waples89_sp deprecated, use Ne_waples89_P2_sp instead.
  • Ne_LD Lists of length three for effective population size, 2.5% and 97.% confidence interval for cutoff allele frequency 0., 0.01, 0.02 and 0.05 (as dictionary keys), using a parametric method, estimated from linkage disequilibrim information of one sample, using LD method developed by Waples & Do 2006 (LDNe). This method assumes unlinked loci and uses LD measured from genotypes at loci. Because this is a sample based method, it should better be applied to a random sample of the population. 95% CI is calculated using a Jackknife estimated effective number of independent alleles. Please refer to relevant papers and the LDNe user’s guide for details.
  • Ne_LD_sp Estimate LD-based effective population size for each specified (virtual) subpopulation.
  • Ne_LD_mono A version of Ne_LD that assumes monogamy (see Waples 2006 for details.
  • Ne_LD_mono_sp Ne_LD_mono calculated for each (virtual) subpopulation.