Some applications of genetics in statistical ecology

Fewster, Rachel M.

doi:10.1007/s10182-016-0273-0

Cited by 5 publications

(8 citation statements)

References 37 publications

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“…The disadvantage of this method is that it becomes particularly inaccurate in the lower tail of the distribution. One feature of the LGP distribution is that many populations have a large number of alleles that occur with very low frequency (Rannala and Mountain, ; Fewster, ). These low‐frequency alleles lead to multilocus LGPs spanning many orders of magnitude in the lower tail.…”

Section: Quantile Methods For Plotting Individuals With Missing Datamentioning

confidence: 99%

“…Now

false(a | p false) \sim normalMultinomial false(2, p false)

, so the marginal posterior distribution of the single‐locus genotype

bolda

, given the reference allele frequencies

boldx

, is a Dirichlet‐compound‐multinomial (DCM) distribution (see Fewster, ). The probability in simplifies to:

\begin{matrix} right double-struckℙ (bolda) = ({, \begin{matrix} left \frac{(x_{r} + τ) (x_{r} + τ + 1)}{(2 n_{R} + k τ) (2 n_{R} + k τ + 1)} & left a_{r} = 2; a_{j} = 0 for j \neq r; \\ left \frac{2 (x_{r} + τ) (x_{s} + τ)}{(2 n_{R} + k τ) (2 n_{R} + k τ + 1)} & left a_{r} = a_{s} = 1; \\ left & left a_{j} = 0 for j \neq r, s . \end{matrix}) & center & left \end{matrix}

…”

Section: Genetic Assignmentmentioning

confidence: 99%

“…Adding the terms in assumes independence among the loci. As with the assumption of independence of alleles within genotypes, any non‐independence (termed linkage disequilibrium ) is unlikely to cause problems unless it is extreme (Fewster, ). Assessment of the impact of linkage disequilibrium is provided in Web Appendix G.…”

Section: Genetic Assignmentmentioning

confidence: 99%

“…Genetic assignment studies typically involve examination of several polymorphic genetic loci, usually microsatellites (Rannala and Mountain, ; Fewster, ). The basis of the analysis is that separate populations have different allele frequencies at these loci.…”

Section: Introductionmentioning

confidence: 99%

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Visualizations for Genetic Assignment Analyses Using the Saddlepoint Approximation Method

McMillan

Fewster

2017

Biometrics

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We propose a method for visualizing genetic assignment data by characterizing the distribution of genetic profiles for each candidate source population. This method enhances the assignment method of Rannala and Mountain (1997) by calculating appropriate graph positions for individuals for which some genetic data are missing. An individual with missing data is positioned in the distributions of genetic profiles for a population according to its estimated quantile based on its available data. The quantiles of the genetic profile distribution for each population are calculated by approximating the cumulative distribution function (CDF) using the saddlepoint method, and then inverting the CDF to get the quantile function. The saddlepoint method also provides a way to visualize assignment results calculated using the leave-one-out procedure. This new method offers an advance upon assignment software such as geneclass2, which provides no visualization method, and is biologically more interpretable than the bar charts provided by the software structure. We show results from simulated data and apply the methods to microsatellite genotype data from ship rats (Rattus rattus) captured on the Great Barrier Island archipelago, New Zealand. The visualization method makes it straightforward to detect features of population structure and to judge the discriminative power of the genetic data for assigning individuals to source populations.

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Section: Quantile Methods For Plotting Individuals With Missing Datamentioning

confidence: 99%

“…Now

false(a | p false) \sim normalMultinomial false(2, p false)

, so the marginal posterior distribution of the single‐locus genotype

bolda

, given the reference allele frequencies

boldx

, is a Dirichlet‐compound‐multinomial (DCM) distribution (see Fewster, ). The probability in simplifies to:

\begin{matrix} right double-struckℙ (bolda) = ({, \begin{matrix} left \frac{(x_{r} + τ) (x_{r} + τ + 1)}{(2 n_{R} + k τ) (2 n_{R} + k τ + 1)} & left a_{r} = 2; a_{j} = 0 for j \neq r; \\ left \frac{2 (x_{r} + τ) (x_{s} + τ)}{(2 n_{R} + k τ) (2 n_{R} + k τ + 1)} & left a_{r} = a_{s} = 1; \\ left & left a_{j} = 0 for j \neq r, s . \end{matrix}) & center & left \end{matrix}

…”

Section: Genetic Assignmentmentioning

confidence: 99%

Section: Genetic Assignmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Visualizations for Genetic Assignment Analyses Using the Saddlepoint Approximation Method

McMillan

Fewster

2017

Biometrics

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“…We briefly summarise the contributions below. Fewster (2017) provides an introduction to statistical analysis of genetic data in ecology, at a level appropriate for statisticians with no previous knowledge of genetics. Statistical analysis of genetic data is too large a subject to be covered in a single paper; this paper restricts attention to multilocus genotype data from microsatellite loci, considering how they are used to address two particular problems: (a) investigating population structure by genetic assignment and related techniques, and (b) using genotype data in capture-recapture studies for estimating population size and demographic parameters.…”

mentioning

confidence: 99%

Guest editors’ introduction to the special issue on “Ecological Statistics”

Langrock

Borchers

2017

AStA Adv Stat Anal

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This special issue is a product of discussions between us and the journal editors after observing that only relatively few statisticians in Germany work on ecological data, in contrast (i) to other countries such as the UK, the US or Australia, where there are several large interdisciplinary research groups working almost exclusively on statistical ecology, and (ii) to other disciplines such as economics and medicine.In response to technological and general scientific developments-e.g. in the areas of remote sensing, satellite imaging, camera trapping and genotyping-the discipline of ecology is experiencing a small revolution driven by the new types of data generated by the technology. Many of these new data types, e.g. animal location data collected using global positioning systems (GPS), clearly contain important information on ecological processes and their drivers. Moreover, technology now enables biologists to study species such as marine mammals at much higher spatial and temporal resolution than was possible using more conventional data collection methods.These new kinds of data often generate new challenges for statisticians, such as dealing with complex dependence structures in space and time, missing data and measurement error, to name but a few. As a consequence, there is a rapidly growing demand within the field of ecology for statistical expertise and innovation. Here we argue, perhaps somewhat provocatively, that this demand is not currently being met within Germany and call for a greater exchange of ideas between ecologists and statisticians. B Roland Langrock

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Wildlife Population Assessment: Changing Priorities Driven by Technological Advances

Buckland

Borchers

Marques

et al. 2023

J Stat Theory Pract

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Advances in technology are having a large effect on the priorities for innovation in statistical ecology. Collaborations between statisticians and ecologists have always been important in driving methodological development, but increasingly, expertise from computer scientists and engineers is also needed. We discuss changes that are occurring and that may occur in the future in surveys for estimating animal abundance. As technology advances, we expect classical distance sampling and capture-recapture to decrease in importance, as camera (still and video) survey, acoustic survey, spatial capture-recapture and genetic methods continue to develop and find new applications. We explore how these changes are impacting the work of the statistical ecologist.

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Some applications of genetics in statistical ecology

Cited by 5 publications

References 37 publications

Visualizations for Genetic Assignment Analyses Using the Saddlepoint Approximation Method

Visualizations for Genetic Assignment Analyses Using the Saddlepoint Approximation Method

Guest editors’ introduction to the special issue on “Ecological Statistics”

Wildlife Population Assessment: Changing Priorities Driven by Technological Advances

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