scholarly journals Quantifying the selection regime in a natural Chironomus riparius population

2020 ◽  
Author(s):  
Markus Pfenninger ◽  
Quentin Foucault
Keyword(s):  
Natural Population ◽  
Time Series Data ◽  
Natural Populations ◽  
Haplotype Frequency ◽  
Chironomus Riparius ◽  
Series Data ◽  
Fluctuating Selection ◽  
Selection Pressures ◽  
Variable Environment ◽  
Selection Regime

SummaryWhile the evolutionary fitness of natural populations is affected by a multitude of environmental factors, theory predicts that selective responses are in principle limited. However, we lack empirical knowledge on the magnitude of different selection pressures natural populations adaptively track. Here, we developed a framework to investigate the quantitative and qualitative complexity of the effectively acting selection regime using population genomic time series data. We applied the approach to a natural population of the multivoltine midge Chironomus riparius. Using six seasonal samples over three years from the same natural population, we could show with fitness experiments that the population continuously evolved in response to a highly variable environment. Analyses of genome-wide allele-frequencies revealed that tens of thousands of haplotypes responded at least once to selection during the monitored period. Clustering the temporal haplotype frequency trajectories revealed 46 different patterns, i.e. selection pressures. Some of these co-varied with measured environmental variables known to be selective factors for the species. Our results demonstrate that 1) adaptive tracking of multiple fluctuating selection pressures occurs in natural populations, 2) the estimated minimum number of simultaneously acting selective pressures is quite high but appears to be limited and 3) changes in intensity and direction of selective responses can be frequent. This shows that adaptation in natural populations can be rapid, pervasive and complex

Density dependence, regulation and variability in animal populations

1990 ◽  
Vol 330 (1257) ◽  
pp. 141-150 ◽  
Keyword(s):  
Density Dependence ◽  
Population Regulation ◽  
Time Series Data ◽  
Complex Dynamics ◽  
Natural Populations ◽  
Single Species ◽  
Abundant Species ◽  
Series Data ◽  
Animal Populations ◽  
The Common

This paper reviews a series of approaches to the study of density dependence, regulation and variability in terrestrial animals, by using single-species, multispecies and life table time series data. Special emphasis is given to the degree of density dependence in the level of variability, which is seldom discussed in this context, but which is conceptually related to population regulation. Broad patterns in density dependence, regulation and variability in vertebrates and arthropods are described, with some more specific results for moths and aphids. Vertebrates have generally less variable populations than arthropods, which is the only well documented, consistent pattern in population variability. The degree of density dependence of variability is negatively correlated with the average level of variability, suggesting that generally the more regulated populations are less variable. Most population studies, especially on insects, have involved outbreak species with complex dynamics, which may explain the common failures to detect density dependence in natural populations. In British moths, density dependence is less obvious in the more abundant species. The study of uncommon and rare species remains a major challenge for population ecology.

scholarly journals Estimating time-varying selection coefficients from time series data of allele frequencies

2020 ◽  
Author(s):  
Iain Mathieson
Keyword(s):  
Time Series ◽  
East Asia ◽  
Ancient Dna ◽  
Experimental Evolution ◽  
Time Series Data ◽  
Natural Populations ◽  
Allele Frequencies ◽  
Series Data ◽  
Alcohol Metabolism ◽  
The Past

AbstractTime series data of allele frequencies are a powerful resource for detecting and classifying natural and artificial selection. Ancient DNA now allows us to observe these trajectories in natural populations of long-lived species such as humans. Here, we develop a hidden Markov model to infer selection coefficients that vary over time. We show through simulations that our approach can accurately estimate both selection coefficients and the timing of changes in selection. Finally, we analyze some of the strongest signals of selection in the human genome using ancient DNA. We show that the European lactase persistence mutation was selected over the past 5,000 years with a selection coefficient of 2-2.5% in Britain, Central Europe and Iberia, but not Italy. In northern East Asia, selection at the ADH1B locus associated with alcohol metabolism intensified around 4,000 years ago, approximately coinciding with the introduction of rice-based agriculture. Finally, a derived allele at the FADS locus was selected in parallel in both Europe and East Asia, as previously hypothesized. Our approach is broadly applicable to both natural and experimental evolution data and shows how time series data can be used to resolve fine-scale details of selection.

scholarly journals Phytoplankton functional types: a trait perspective

2017 ◽  
Author(s):  
Andrew J Irwin ◽  
Zoe V Finkel
Keyword(s):  
Time Series ◽  
Statistical Models ◽  
Role Models ◽  
Time Series Data ◽  
Natural Populations ◽  
Series Data ◽  
Potential Solution ◽  
Number Of Species ◽  
Fitness Traits ◽  
Functional Types

AbstractPhytoplankton functional types are groupings of many species into a smaller number of types according to their ecological or biogeochemical role. Models describe phytoplankton functional types by a set of traits that determine their growth rates or fitness. Traits for functional types are often determined from observations on a small number of species under laboratory conditions. Functional types can be composed of a large number of species with very different trait values, so the representation of a type by an average trait value may not be appropriate. A potential solution is to estimate trait values from observations of the aggregate biomass of phytoplankton functional types in natural populations. We report on some recent efforts to extract trait values from time-series data using Bayesian statistical models and discuss some challenges of this approach.

scholarly journals A population-genomic approach for estimating selection on polygenic traits in heterogeneous environments

2020 ◽  
Author(s):  
Zachariah Gompert
Keyword(s):  
Time Series Data ◽  
Computer Software ◽  
Phenotypic Selection ◽  
Evolutionary Change ◽  
Series Data ◽  
Heterogeneous Environments ◽  
Ecological Specialization ◽  
Fluctuating Selection ◽  
Method Performance ◽  
Population Genomic

AbstractStrong selection can cause rapid evolutionary change, but temporal fluctuations in the form, direction and intensity of selection can limit net evolutionary change over longer time periods. Fluctuating selection could affect molecular diversity levels and the evolution of plasticity and ecological specialization. Nonetheless, this phenomenon remains understudied, in part because of analytical limitations and the general difficulty of detecting selection that does not occur in a consistent manner. Herein, I fill this analytical gap by presenting an approximate Bayesian computation (ABC) method to detect and quantify fluctuating selection on poly-genic traits from population-genomic time-series data. I propose a model for environment-dependent phenotypic selection. The evolutionary genetic consequences of selection are then modeled based on a genotype-phenotype map. Using simulations, I show that the proposed method generates accurate and precise estimates of selection when the generative model for the data is similar to the model assumed by the method. Performance of the method when applied to an evolve-and-resequence study of host adaptation in the cowpea seed beetle (Cal-losobruchus maculatus) was more idiosyncratic and depended on specific analytical choices. Despite some limitations, these results suggest the proposed method provides a powerful approach to connect causes of (variable) selection to traits and genome-wide patterns of evolution. Documentation and open source computer software (fsabc) implementing this method are available from GitHub (https://github.com/zgompert/fsabc.git).

scholarly journals Detecting alternative attractors in ecosystem dynamics

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Torbjörn Säterberg ◽  
Kevin McCann
Keyword(s):  
Population Dynamics ◽  
Time Series Data ◽  
Abiotic Factors ◽  
Natural Populations ◽  
Ecosystem Dynamics ◽  
Series Data ◽  
Natural Systems ◽  
Experimental Systems ◽  
Dynamical Behaviors ◽  
Natural Time

AbstractDynamical systems theory suggests that ecosystems may exhibit alternative dynamical attractors. Such alternative attractors, as for example equilibria and cycles, have been found in the dynamics of experimental systems. Yet, for natural systems, where multiple biotic and abiotic factors simultaneously affect population dynamics, it is more challenging to distinguish alternative dynamical behaviors. Although recent research exemplifies that some natural systems can exhibit alternative states, a robust methodology for testing whether these constitute distinct dynamical attractors is currently lacking. Here, using attractor reconstruction techniques we develop such a test. Applications of the methodology to simulated, experimental and natural time series data, reveal that alternative dynamical behaviors are hard to distinguish if population dynamics are governed by purely stochastic processes. However, if population dynamics are brought about also by mechanisms internal to the system, alternative attractors can readily be detected. Since many natural populations display evidence of such internally driven dynamics, our approach offers a method for empirically testing whether ecosystems exhibit alternative dynamical attractors.

Sex ratio dynamics and fluctuating selection pressures in natural populations of the Trinidadian guppy, Poecilia reticulata

2004 ◽  
Vol 55 (5) ◽  
pp. 461-468 ◽  
Author(s):  
Lars B. Pettersson ◽  
Indar W. Ramnarine ◽  
S. Anette Becher ◽  
Rajindra Mahabir ◽  
Anne E. Magurran
Keyword(s):  
Sex Ratio ◽  
Poecilia Reticulata ◽  
Natural Populations ◽  
Fluctuating Selection ◽  
Selection Pressures ◽  
Trinidadian Guppy

scholarly journals The Tempo and Mode of Adaptation in a Complex Natural Population: the Microbiome

mSystems ◽  
2021 ◽  
Author(s):  
Nandita Garud
Keyword(s):  
Natural Population ◽  
Natural Populations ◽  
Selection Pressures ◽  
Ecological Changes ◽  
Fundamental Process

Adaptation is a fundamental process by which populations evolve to grow more fit in their environments. Recent studies are starting to show us that commensal microbes can evolve on short timescales of days and months, suggesting that ecological changes are not the only means by which microbes in complex natural populations respond to selection pressures.

scholarly journals Statistical analysis of the dynamics of antibody loss to a disease-causing agent: plague in natural populations of great gerbils as an example

2006 ◽  
Vol 4 (12) ◽  
pp. 57-64 ◽  
Author(s):  
Siyun Park ◽  
Kung-Sik Chan ◽  
Hildegunn Viljugrein ◽  
Larissa Nekrassova ◽  
Bakhtiyar Suleimenov ◽  
...  
Keyword(s):  
Time Series Data ◽  
Serological Test ◽  
Natural Populations ◽  
Winter Season ◽  
Series Data ◽  
Serological Tests ◽  
Mixed Effect ◽  
Nonlinear Mixed Effect Model ◽  
Nonlinear Mixed Effect ◽  
Stochastic Framework

We propose a new stochastic framework for analysing the dynamics of the immunity response of wildlife hosts against a disease-causing agent. Our study is motivated by the need to analyse the monitoring time-series data covering the period from 1975 to 1995 on bacteriological and serological tests—samples from great gerbils being the main host of Yersinia pestis in Kazakhstan. Based on a four-state continuous-time Markov chain, we derive a generalized nonlinear mixed-effect model for analysing the serological test data. The immune response of a host involves the production of antibodies in response to an antigen. Our analysis shows that great gerbils recovered from a plague infection are more likely to keep their antibodies to plague and survive throughout the summer-to-winter season than throughout the winter-to-summer season. Provided the seasonal mortality rates are similar (which seems to be the case based on a mortality analysis with abundance data), our finding indicates that the immune function of the sampled great gerbils is seasonal.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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