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36 results for “demographics estimation”
Data from: The complex effects of demographic history on the estimation of substitution rate: concatenated gene analysis results in no more than twofold overestimation
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Data from: Quantifying the importance of geographic replication and representativeness when estimating demographic rates, using a coastal species as a case study
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Estimated values of boreal caribou demographic rates (R, S, lambda) generated from SpaDES landscape simulations in the Northwest Territories
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Data from: Estimating latent individual demographic heterogeneity using structural equation models
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A practical introduction to sequentially Markovian coalescent methods for estimating demographic history from genomic data
<p>A common goal of population genomics and molecular ecology is to reconstruct the demographic history of a species of interest. A pair of powerful tools based on the sequentially Markovian coalescent have been developed to infer past population sizes using genome sequences. These methods are most useful when sequences are available for only a limited number of genomes and when the aim is to study ancient demographic events. The results of these analyses can be difficult to interpret accurately, because doing so requires some understanding of their theoretical basis and of their sensitivity to confounding factors. In this practical review, we explain some of the key concepts underpinning the pairwise and multiple sequentially Markovian coalescent methods (PSMC and MSMC, respectively). We relate these concepts to the use and interpretation of these methods, and we explain how the choice of different parameter values by the user can affect the accuracy and precision of the inferences. Based on our survey of 100 PSMC studies and 30 MSMC studies, we describe how the two methods are used in practice. Readers of this article will become familiar with the principles, practice, and interpretation of the sequentially Markovian coalescent for inferring demographic history.</p>
Data from: Genotype-free estimation of allele frequencies reduces bias and improves demographic inference from RADSeq data
Restriction-site associated sequencing (RADSeq) facilitates rapid generation of thousands of genetic markers at relatively low cost; however, several sources of error specific to RADSeq methods often lead to biased estimates of allele frequencies and thereby to erroneous population genetic inference. Estimating the distribution of sample allele frequencies without calling genotypes was shown to improve population inference from whole genome sequencing data, but the ability of this approach to account for RADSeq-specific biases remains unexplored. Here we assess in how far genotype-free methods of allele frequency estimation affect demographic inference from empirical RADSeq data. Using the well-studied pied flycatcher (Ficedula hypoleuca) as a study system, we compare allele frequency estimation and demographic inference from whole genome sequencing data with that from RADSeq data matched for samples using both genotype-based and genotype free methods. The demographic history of pied flycatchers as inferred from RADSeq data was highly congruent with that inferred from WGS data when allele frequencies were estimated directly from the read data. In contrast, when allele frequencies were derived from called genotypes, RADSeq-based estimates of most model parameters fell outside the 95% confidence interval (CI) of estimates derived from WGS data. Notably, more stringent filtering of genotypes tended to increase the discrepancy between parameter estimates from WGS and RADSeq data, respectively. The results from this study demonstrate the ability of genotype-free methods to improve AFS-based demographic inference from RADSeq data and highlight the need to account for uncertainty in NGS data regardless of sequencing method.
Estimating correlations among demographic parameters in population models
<p>Estimating correlations among demographic parameters is critical to understanding population dynamics and life-history evolution, where correlations among parameters can inform our understanding of life-history trade-offs, result in effective applied conservation actions, and shed light on evolutionary ecology. The most common approaches rely on the multivariate normal distribution, and its conjugate inverse Wishart prior distribtion. However, the inverse Wishart prior for the covariance matrix of multivariate normal distributions has a strong influence on posterior distributions. As an alternative to the inverse Wishart distribution, we individually parameterize the covariance matrix of a multivariate normal distribution to accurately estimate variances (σ<sup>2</sup>) of, and process correlations (ρ) between, demographic parameters. We evaluate this approach using simulated capture-mark-recapture data. We then use this method to examine process correlations between adult and juvenile survival of black brent marked on the Yukon-Kuskokwim River Delta, Alaska (1988-2014). Our parameterization consistently outperformed the conjugate inverse Wishart prior for simulated data, where the means of posterior distributions estimated using an inverse Wishart prior were substantially different from the values used to simulate the data. Brent adult and juvenile annual apparent survival rates were strongly positively correlated (ρ = 0.563, 95% CRI 0.181 − 0.823), suggesting that habitat conditions have significant effects on both adult and juvenile survival. We provide robust simulation tools, and our methods can readily be expanded for use in other capture-recapture or capture-recovery frameworks. Further, our work reveals limits on the utility of these approaches when study duration or sample sizes are small.</p>
Data from: Integrated population models poorly estimate the demographic contribution of immigration
<p class="BodyA">Estimating the contribution of demographic parameters to changes in population growth is essential for understanding why populations fluctuate. Integrated Population Models (IPMs) offer a possibility to estimate contributions of additional demographic parameters, for which no data have been explicitly collected: typically immigration. Such parametersare often subsequently highlighted as important drivers of population growth. Yet, accuracy in estimating their temporal variation, and consequently their contribution to changes in population growth rate, has not been investigated.</p> <p class="BodyA">To quantify the magnitude and cause of potential biases when estimating the contribution of immigration using IPMs, we simulated data (using Northern Wheatear <i>Oenanthe oenanthe </i>population estimates) from controlled scenarios to examine potential biases and how they depend on IPM parameterization, formulation of priors, the level of temporal variation in immigration, and sample size. We also used empirical data on populations with known rates of immigration: Soay Sheep <i>Ovis aries</i> and Mauritius kestrel <i>Falco punctatus</i> with zero immigration and<i> </i>grey wolf <i>Canis lupus</i> in Scandinavia with<i> </i>near-zero immigration.</p> <p class="Default">IPMs strongly overestimated the contribution of immigration to changes in population growth in scenarios when immigration was simulated with zero temporal variation (proportion of variance attributed to immigration = 63% for the more constrained formulation and real sample size) and in the wild populations, where the true number of immigrants was zero or near-zero (Kestrel 19.1-98.2%, Sheep 4.2-36.1%, Wolf 84.0-99.2%). Although the estimation of immigration in the simulation study became more accurate with increasing temporal variation and sample size, it was often not possible to distinguish between an accurate estimation from data with high temporal variation versus an overestimation from data with low temporal variation. Unrealistically large sample sizes may be required to estimate the contribution of immigration well.</p> <p class="Body">To minimise the risk of overestimating the contribution of immigration (or any additional parameter) in IPMs, we recommend to: (i) look for evidence of variation in immigration before investigating its contribution to population growth, (ii) simulate and model data for comparison to the real data, and (iii) use explicit data on immigration when possible.</p>
Data from: Implications of survey effort on estimating demographic parameters of a long-lived marine top predator
Effective management of wildlife populations rely on knowledge of their abundance, survival and reproductive rates. Maintaining long-term studies capable of estimating demographic parameters for long-lived, slow reproducing species is challenging. Insights into effects of research intensity on the statistical power to estimate demographic parameters is limited. Here, we investigate implications of survey effort on estimating abundance, home range sizes and reproductive output of Indo-Pacific bottlenose dolphins (Tursiops aduncus), using a three-year sub-sample of a long-term, capture-recapture study off Bunbury, Western Australia. Photo-identification on individual dolphins were collected following Pollock's Robust Design, where seasons were defined as 'primary periods', each consisting of multiple 'secondary periods'. The full dataset consisted of 12 primary periods and 72 secondary periods, resulting in the study area being surveyed 24 times/year. We simulated reduced survey effort by randomly removing one, two or three secondary periods per primary period. Capture-recapture models were used to assess the effect of survey intensity on the power to detect trends in population abundance, while individual dolphin sighting histories were used to assess the ability to conduct home range analyses. We used sighting records of adult females and their calving histories to assess survey effort on quantifying reproductive output. A 50% reduction in survey effort resulted in: i) up to a 36% decline in population abundance at the time of detection; ii) a reduced ability to estimate home range sizes, by increasing the time for individuals to be sighted on >30 occasions (an often-used metric for home range analyses) from 7.74 to 15.46 years; and iii) 33%, 24% and 33% of annual calving events going undocumented in their year of birth. Results clearly illustrate the importance of survey effort on the ability to assess demographic parameters with clear implications for population viability analyses, population forecasting and conservation efforts to manage human-wildlife interactions.
A practical introduction to sequentially Markovian coalescent methods for estimating demographic history from genomic data
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Data from: Genotype-free estimation of allele frequencies reduces bias and improves demographic inference from RADSeq data
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Data from: Region-wide and ecotype-specific differences in demographic histories of threespine stickleback populations, estimated from whole genome sequences
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Data from: Implications of survey effort on estimating demographic parameters of a long-lived marine top predator
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Data from: Estimating genome-wide heterozygosity: effects of demographic history and marker type
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Estimating correlations among demographic parameters in population models
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Data from: Integrated population models poorly estimate the demographic contribution of immigration
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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