29,235 research outputs found
Integral projection models for species with complex demography
Matrix projection models occupy a central role in population and conservation biology. Matrix models divide a population into discrete classes, even if the structuring trait exhibits continuous variation ( e. g., body size). The integral projection model ( IPM) avoids discrete classes and potential artifacts from arbitrary class divisions, facilitates parsimonious modeling based on smooth relationships between individual state and demographic performance, and can be implemented with standard matrix software. Here, we extend the IPM to species with complex demographic attributes, including dormant and active life stages, cross- classification by several attributes ( e. g., size, age, and condition), and changes between discrete and continuous structure over the life cycle. We present a general model encompassing these cases, numerical methods, and theoretical results, including stable population growth and sensitivity/ elasticity analysis for density- independent models, local stability analysis in density- dependent models, and optimal/ evolutionarily stable strategy life- history analysis. Our presentation centers on an IPM for the thistle Onopordum illyricum based on a 6- year field study. Flowering and death probabilities are size and age dependent, and individuals also vary in a latent attribute affecting survival, but a predictively accurate IPM is completely parameterized by fitting a few regression equations. The online edition of the American Naturalist includes a zip archive of R scripts illustrating our suggested methods
Bayesian inference of natural selection from allele frequency time series
The advent of accessible ancient DNA technology now allows the direct
ascertainment of allele frequencies in ancestral populations, thereby enabling
the use of allele frequency time series to detect and estimate natural
selection. Such direct observations of allele frequency dynamics are expected
to be more powerful than inferences made using patterns of linked neutral
variation obtained from modern individuals. We develop a Bayesian method to
make use of allele frequency time series data and infer the parameters of
general diploid selection, along with allele age, in non-equilibrium
populations. We introduce a novel path augmentation approach, in which we use
Markov chain Monte Carlo to integrate over the space of allele frequency
trajectories consistent with the observed data. Using simulations, we show that
this approach has good power to estimate selection coefficients and allele age.
Moreover, when applying our approach to data on horse coat color, we find that
ignoring a relevant demographic history can significantly bias the results of
inference. Our approach is made available in a C++ software package.Comment: 27 page
The Effects of Population Size Histories on Estimates of Selection Coefficients from Time-Series Genetic Data.
Many approaches have been developed for inferring selection coefficients from time series data while accounting for genetic drift. These approaches have been motivated by the intuition that properly accounting for the population size history can significantly improve estimates of selective strengths. However, the improvement in inference accuracy that can be attained by modeling drift has not been characterized. Here, by comparing maximum likelihood estimates of selection coefficients that account for the true population size history with estimates that ignore drift by assuming allele frequencies evolve deterministically in a population of infinite size, we address the following questions: how much can modeling the population size history improve estimates of selection coefficients? How much can mis-inferred population sizes hurt inferences of selection coefficients? We conduct our analysis under the discrete Wright-Fisher model by deriving the exact probability of an allele frequency trajectory in a population of time-varying size and we replicate our results under the diffusion model. For both models, we find that ignoring drift leads to estimates of selection coefficients that are nearly as accurate as estimates that account for the true population history, even when population sizes are small and drift is high. This result is of interest because inference methods that ignore drift are widely used in evolutionary studies and can be many orders of magnitude faster than methods that account for population sizes
Decomposing change in life expectancy: a bouquet of formulas in honour of Nathan Keyfitz´s 90th birthday
-Japan, Sweden, causes of death, life expectancy
πBUSS:a parallel BEAST/BEAGLE utility for sequence simulation under complex evolutionary scenarios
Background: Simulated nucleotide or amino acid sequences are frequently used
to assess the performance of phylogenetic reconstruction methods. BEAST, a
Bayesian statistical framework that focuses on reconstructing time-calibrated
molecular evolutionary processes, supports a wide array of evolutionary models,
but lacked matching machinery for simulation of character evolution along
phylogenies.
Results: We present a flexible Monte Carlo simulation tool, called piBUSS,
that employs the BEAGLE high performance library for phylogenetic computations
within BEAST to rapidly generate large sequence alignments under complex
evolutionary models. piBUSS sports a user-friendly graphical user interface
(GUI) that allows combining a rich array of models across an arbitrary number
of partitions. A command-line interface mirrors the options available through
the GUI and facilitates scripting in large-scale simulation studies. Analogous
to BEAST model and analysis setup, more advanced simulation options are
supported through an extensible markup language (XML) specification, which in
addition to generating sequence output, also allows users to combine simulation
and analysis in a single BEAST run.
Conclusions: piBUSS offers a unique combination of flexibility and
ease-of-use for sequence simulation under realistic evolutionary scenarios.
Through different interfaces, piBUSS supports simulation studies ranging from
modest endeavors for illustrative purposes to complex and large-scale
assessments of evolutionary inference procedures. The software aims at
implementing new models and data types that are continuously being developed as
part of BEAST/BEAGLE.Comment: 13 pages, 2 figures, 1 tabl
Are any growth theories linear? Why we should care about what the evidence tells us
Recent research on macroeconomic growth has been focused on resolving several key issues, two of which, specification uncertainty of the growth process and variable uncertainty, have received much attention in the recent literature. The standard procedure has been to assume a linear growth process and then to proceed with investigating the relevant variables that determine growth across countries. However, a more appropriate approach would be to recognize that a misspecified model may lead one to conclude that a variable is relevant when in fact it is not. This paper takes a step in this direction by considering conditional variable uncertainty with full blown specification uncertainty. We use recently developed nonparametric model selection techniques to deal with nonlinearities and competing growth theories. We show how one can interpret our results and use them to motivate more intriguing specifications within the traditional studies that use Bayesian Model Averaging or other model selection criteria. We find that the inclusion of nonlinearities is necessary for determining the empirically relevant variables that dictate growth and that nonlinearities are especially important in uncovering key mechanism of the growth process.Growth Nonlinearities, Irrelevant Variables, Least Squares Cross Validation, Bayesian Model Averaging, Parameter Heterogeneity
Evolution of complex flowering strategies: an age- and size-structured integral projection model
We explore the evolution of delayed age- and size-dependent flowering in the monocarpic perennial Carlina vulgaris, by extending the recently developed integral projection approach to include demographic rates that depend on size and age. The parameterized model has excellent descriptive properties both in terms of the population size and in terms of the distributions of sizes within each age class. In Carlina the probability of flowering depends on both plant size and age. We use the parameterized model to predict this relationship, using the evolutionarily stable strategy (ESS) approach. Despite accurately predicting the mean size of flowering individuals, the model predicts a step-function relationship between the probability of flowering and plant size, which has no age component. When the variance of the flowering-threshold distribution is constrained to the observed value, the ESS flowering function contains an age component, but underpredicts the mean flowering size. An analytical approximation is used to explore the effect of variation in the flowering strategy on the ESS predictions. Elasticity analysis is used to partition the agespecific contributions to the finite rate of increase (u) of the survival-growth and fecundity components of the model. We calculate the adaptive landscape that defines the ESS and generate a fitness landscape for invading phenotypes in the presence of the observed flowering strategy. The implications of these results for the patterns of genetic diversity in the flowering strategy and for testing evolutionary models are discussed. Results proving the existence of a dominant eigenvalue and its associated eigenvectors in general size- and age-dependent integral projection models are presented
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