Linking micro and macroevolution in the presence of migration

The process of speciation is of key importance in evolutionary biology because it shapes macroevolutionary patterns. This process starts at the microevolutionary level, for instance, when two subpopulations evolve towards different phenotypic optima. The speed at which these optima are reached is controlled by the degree of stabilising selection, which pushes a mean trait towards an optimum within subpopulations, and ongoing migration that pulls the mean phenotype away from that optimum. Traditionally, macro phenotypic evolution with selection has been modelled by Ornstein-Uhlenbeck (OU) processes, but these models have ignored the role of migration within species. Here, our goal is to reconcile the processes of micro and macroevolution by modelling migration during speciation. More precisely, we introduce an OU model where migration happens between two subpopulations within a branch of a phylogeny and this migration decreases over time as it happens during speciation. We then use this model to study the evolution of trait means along a phylogeny, as well as the way phenotypic disparity between species changes with successive epochs. We show that ignoring the effect of migration in sampled time-series data leads to a significant underestimation of the selective forces acting upon it. We also show that migration decreases the expected phenotypic disparity between species and we show the effect of migration in the particular case of niche filling. We further introduce a method to jointly estimate selection and migration from time-series data. Our model extends standard results of interactions selection-migration in a microevolutionary time frame across multiple speciation events at a macroevolutionary scale. Our results further proof that not accounting for gene flow has important consequences in inferences at both the micro and macroevolutionary scale.

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Distance variable improvement of time-series big data stream evaluation

Journal Of Big Data ◽

10.1186/s40537-020-00359-w ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Ari Wibisono ◽

Petrus Mursanto ◽

Jihan Adibah ◽

Wendy D. W. T. Bayu ◽

May Iffah Rizki ◽

...

Keyword(s):

Time Series ◽

Standard Deviation ◽

Standard Method ◽

Time Series Data ◽

Series Data ◽

Percentage Error ◽

Traffic Demand ◽

Incremental Model ◽

Chernoff Bound ◽

The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

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PENGARUH KUNJUNGAN WISATAWAN DAN RATA-RATA PENGELUARAN WISATAWAN TERHADAP PAD DAN PERTUMBUHAN EKONOMI PROVINSI BALI

E-Jurnal Ekonomi dan Bisnis Universitas Udayana ◽

10.24843/eeb.2021.v10.i06.p02 ◽

2021 ◽

pp. 511

Author(s):

I Gede Dea Joendra Septyana Putra ◽

Ni Luh Karmini ◽

I Wayan Wenagama

Keyword(s):

Economic Growth ◽

Time Series ◽

Time Series Data ◽

Secondary Data ◽

Data Sources ◽

Series Data ◽

Local Revenue ◽

Analysis Technique ◽

Secondary Data Sources

This study aims to analyze the effect of the number of tourist visits and the average tourist expenditure on the local income of Bali Province, to analyze the effect of the number of tourist visits, average tourist expenditure, and local income on the economic growth of Bali Province, and to analyze the role of income. native areas in mediating the effect of the number of tourist visits and the average tourist expenditure on the economic growth of Bali Province. The data used in this research is secondary data, with the method of observation by observing documents or secondary data sources that are related. This study uses time series data with a total of 30 years of observations from 1990-2019, with the analysis technique used is Path Analysis. This study shows the results that the number of tourist visits and the average tourist expenditure have a positive and significant effect on local income in Bali Province. The number of tourist visits, the average tourist expenditure and local revenue have a positive and significant effect on economic growth in Bali Province. Own-source revenue mediates the effect of the number of tourist visits and the average tourist expenditure on economic growth in Bali Province.

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DOI: https://doi.org/10.36873/ae , Frits Fahridws Damanik

Agrienvi, Jurnal Ilmu Pertanian ◽

10.36873/aev.v13i02.723 ◽

2020 ◽

Vol 13 (02) ◽

pp. 1-8

Author(s):

Agrienvi

Keyword(s):

Time Series ◽

Time Series Data ◽

Arima Model ◽

Series Data ◽

Production Data ◽

Policy Makers ◽

Strategic Value ◽

Modeling And Forecasting ◽

The Mean ◽

Arima Modeling

ABSTRACTChili is one of the leading commodities of vegetables which has strategic value at national and regional levels.An unexpected increase in chili prices often results a surge of inflation and economic turmoil. Study and modeling ofchili production are needed as a planning and evaluation material for policy makers. One of the most frequently usedmethods in modeling and forecasting time series data is Autoregressive Integrated Moving Avarage (ARIMA). Theresults of ARIMA modeling on chili production data found that the data were unstationer conditions of the mean so thatmust differenced while the data on the production of small chilli carried out the stages of data transformation anddifferencing due to the unstationer of data on variants and the mean. The best ARIMA model that can be applied basedon the smallest AIC and MSE criteria for data on the amount of chili and small chilli production in Central KalimantanProvince is ARIMA (3,1,0).Keywords: modeling of chilli, forecasting of chilli, Autoregresive Integrated Moving Avarage, ARIMA, Box-Jenkins.

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Resource–Economic Growth Nexus, Role of Governance, Financial Development, Globalisation and War: Dynamic Approach

10.20944/preprints201904.0096.v1 ◽

2019 ◽

Author(s):

Sorush Niknamian

Keyword(s):

Economic Growth ◽

Time Series ◽

Financial Development ◽

Time Series Data ◽

Good Governance ◽

Series Data ◽

Negative Effect ◽

The Government ◽

Rich Countries

This study reassesses the resource–economic growth nexus by incorporating several channels. Advanced panel time series techniques are used to analyse panel time series data from 1980 to 2015 in 31 oil-rich countries. Results show that oil rent augments economic growth; thus, oil rent is conducive rather than impediment for economic growth. The role of governance in economic growth is significant in the selected countries. Oil rent exerts a positive significant impact on economic growth in countries with good governance compare to countries with poor governance. Financial development is an unimportant channel in the resource–growth nexus because FD is often unable to mobilise oil rent from the government to the private sector in oil-rich countries. Globalisation is advantageous for countries and promote economic growth. Moreover, war exerts a significant negative effect on growth in the long term.

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Explaining the Decline of European Social Democracy: The Role of Structural Economic Change

World Politics ◽

10.1017/s0043887100015197 ◽

1995 ◽

Vol 47 (4) ◽

pp. 495-533 ◽

Cited By ~ 40

Author(s):

Jonas Pontusson

Keyword(s):

Time Series ◽

Time Series Data ◽

Economic Change ◽

Social Democracy ◽

European Countries ◽

Series Data ◽

Large Measure ◽

Economic Changes ◽

Social Democratic

Using a number of different quantitative measures, this article demonstrates that variations in the degree of social democratic decline in nine European countries can be viewed in large measure as a product of two structural economic changes: (1) the shift to smaller units of production; and (2) the growth of private nonindustrial employment. The article explores several causal arguments linking these variables to social democratic decline, and it marshals Swedish and British time-series data to show that the distribution of manufacturing employment by production unit helps explain both the rise and the decline of social democracy.

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Early detection of seasonality and second-wave prediction in the COVID-19 pandemic

10.1101/2020.09.02.20187203 ◽

2020 ◽

Author(s):

Márcio Watanabe

Keyword(s):

Growth Rate ◽

Time Series ◽

Southern Hemisphere ◽

Northern Hemisphere ◽

Time Series Data ◽

Seasonal Effects ◽

Series Data ◽

Seasonal Effect ◽

Social Distancing ◽

The Mean

AbstractSeasonality plays an essential role in the dynamics of many infectious diseases. Its confirmation in an emerging infectious disease is usually done using time series data from several years. By using statistical regression methods for time-series data pooled from more than 50 countries from both hemispheres, we show how to determine its presence in a pandemic at the onset of the seasonal period. We measure its expected effect in the mean transmission rate of SARS-coV-2 and predict when further epidemic outbreaks of COVID-19 will occur. The obtained result in the Northern Hemisphere shows that seasonality reduced the mean growth rate in 222.5% in April 2020. A relative reduction greater than 100% should be interpreted as a reduction changing an increasing rate to a decreasing one. In contrast, at the same moment, the seasonal effect in the Southern Hemisphere increased the mean growth rate in 740.3%. Our analysis simultaneously considers other confounding factors to properly separate them from seasonal effects and, in addition, we measure the mean global effect of social-distancing interventions and its relation with income. Future COVID-19 waves are expected to occur in autumn/winter seasons, typically between September and March in the Northern Hemisphere, and between April and September in the Southern Hemisphere. Simulations of a seasonal SEIR model with a social distancing effect are shown to describe the behavior of COVID-19 outbreaks in several countries. These results provide vital information for policy makers to plan their actions against the new coronavirus disease, particularly in the optimization of social-distancing interventions and vaccination schedules. Ultimately, our methods can be used to identify and measure seasonal effects in a future pandemic.

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Functional Data Analysis of Weather Variables Linked to Fusarium Head Blight Epidemics in the United States

Phytopathology ◽

10.1094/phyto-11-17-0386-r ◽

2019 ◽

Vol 109 (1) ◽

pp. 96-110 ◽

Cited By ~ 4

Author(s):

D. A. Shah ◽

E. D. De Wolf ◽

P. A. Paul ◽

L. V. Madden

Keyword(s):

Time Series ◽

Data Analysis ◽

Functional Data Analysis ◽

Functional Data ◽

Time Series Data ◽

The United States ◽

Series Data ◽

Head Blight ◽

Weather Variables ◽

The Mean

In past efforts, input weather variables for Fusarium head blight (FHB) prediction models in the United States were identified after following some version of the window-pane algorithm, which discretizes a continuous weather time series into fixed-length windows before searching for summary variables associated with FHB risk. Functional data analysis, on the other hand, reconstructs the assumed continuous process (represented by a series of recorded weather data) by using smoothing functions, and is an alternative way of working with time series data with respect to FHB risk. Our objective was to functionally model weather-based time series data linked to 865 observations of FHB (covering 16 states and 31 years in total), classified as epidemics (FHB disease index ≥ 10%) and nonepidemics (FHB disease index < 10%). Altogether, 94 different time series variables were modeled by penalized cubic B-splines for the smoothing function, from 120 days pre-anthesis to 20 days post-anthesis. Functional mean curves, standard deviations, and first derivatives were plotted for FHB epidemics relative to nonepidemics. Function-on-scalar regressions assessed the temporal trends of the magnitude and significance of the mean difference between functionally represented weather time series associated with FHB epidemics and nonepidemics. The mean functional weather-variable curve for epidemics started to deviate, in general, from that for nonepidemics as early as 40 days pre-anthesis for several weather variables. The greatest deviations were often near anthesis, the period of maximum susceptibility of wheat to FHB-causing fungi. The most consistent separations between the mean functional curves were seen with the daily averages of moisture-related variables (such as average relative humidity) and with variables summarizing the daily variation in temperature (as opposed to the daily mean). Functional data analysis was useful for extending our knowledge of relationships between weather variables and FHB epidemics.

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Resource–Economic Growth Nexus, Role of Governance, Financial Development, Globalisation and War: Dynamic Approach

10.31219/osf.io/akhsr ◽

2019 ◽

Author(s):

Sorush Niknamian

Keyword(s):

Economic Growth ◽

Time Series ◽

Financial Development ◽

Time Series Data ◽

Good Governance ◽

Series Data ◽

Negative Effect ◽

The Government ◽

Rich Countries

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Exploring the physiologic role of human gastroesophageal reflux by analyzing time-series data from 24-h gastric and esophageal pH recordings

Physiological Reports ◽

10.14814/phy2.12051 ◽

2014 ◽

Vol 2 (7) ◽

pp. e12051 ◽

Cited By ~ 1

Author(s):

Luo Lu ◽

John C. Mu ◽

Sheldon Sloan ◽

Philip B. Miner ◽

Jerry D. Gardner

Keyword(s):

Time Series ◽

Gastroesophageal Reflux ◽

Time Series Data ◽

Series Data ◽

Esophageal Ph ◽

Physiologic Role

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The Influence Of Small Micro Industries On Economic Growth

JEJAK ◽

10.15294/jejak.v12i2.17828 ◽

2019 ◽

Vol 12 (2) ◽

pp. 318-326

Author(s):

Rohadin Rohadin ◽

Yanah Yanah

Keyword(s):

Economic Growth ◽

Time Series ◽

Financial Institutions ◽

Time Series Data ◽

Series Data ◽

Growth Data ◽

Capital Access ◽

The Government ◽

Analyze Data

The purpose of this study to determine whether SMEs have a role to economic growth and how big the role of SMEs to economic growth in Indonesia. Types of data used are time series data i.e SMEs data and Economic growth data from year 2003 until 2018 in Indonesia.Tool of analyze data used in this research is multiple linear regression. The result of analysis shows that the influence between of SMEs on economic growth in Indonesia is only 12,5%, it means that Small Micro Entreprises do not have a significant influence on economic growth in Indonesia, government to accelerate the development of SMEs in Indonesia in order to contribute to economic growth as in the economic crisis that occurred in 1998 SMEs are able to survive when many large companies are bankrupt. This may be caused by SMEs owners and workers in SMEs do not pay taxes to the government so that not much contribute to the economic growth of the Indonesia. In order for SMEs to contribute to economic growth, must export their products to other countries and support from the government is needed to facilitate SMEs in obtaining capital access from financial institutions.

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