scholarly journals Informing management decisions for ecological networks, using dynamic models calibrated to noisy time‐series data

2020 ◽  
Vol 23 (4) ◽  
pp. 607-619 ◽  
Author(s):  
Matthew P. Adams ◽  
Scott A. Sisson ◽  
Kate J. Helmstedt ◽  
Christopher M. Baker ◽  
Matthew H. Holden ◽  
...  
Keyword(s):  
Time Series ◽  
Dynamic Models ◽  
Time Series Data ◽  
Ecological Networks ◽  
Series Data ◽  
Management Decisions ◽  
Noisy Time Series

scholarly journals Reconstructing ecological networks with noisy dynamics

2020 ◽  
Vol 476 (2237) ◽  
pp. 20190739 ◽  
Author(s):  
Mara A. Freilich ◽  
Rolando Rebolledo ◽  
Derek Corcoran ◽  
Pablo A. Marquet
Keyword(s):  
Time Series ◽  
Network Topology ◽  
Network Structure ◽  
Time Series Data ◽  
Species Abundance ◽  
Intrinsic Noise ◽  
Ecological Networks ◽  
Series Data ◽  
Statistical Techniques ◽  
Time Series Data Analysis

Ecosystems functioning is based on an intricate web of interactions among living entities. Most of these interactions are difficult to observe, especially when the diversity of interacting entities is large and they are of small size and abundance. To sidestep this limitation, it has become common to infer the network structure of ecosystems from time series of species abundance, but it is not clear how well can networks be reconstructed, especially in the presence of stochasticity that propagates through ecological networks. We evaluate the effects of intrinsic noise and network topology on the performance of different methods of inferring network structure from time-series data. Analysis of seven different four-species motifs using a stochastic model demonstrates that star-shaped motifs are differentially detected by these methods while rings are differentially constructed. The ability to reconstruct the network is unaffected by the magnitude of stochasticity in the population dynamics. Instead, interaction between the stochastic and deterministic parts of the system determines the path that the whole system takes to equilibrium and shapes the species covariance. We highlight the effects of long transients on the path to equilibrium and suggest a path forward for developing more ecologically sound statistical techniques.

Human fetuses have nonlinear cardiac dynamics

1999 ◽  
Vol 87 (2) ◽  
pp. 530-537 ◽  
Author(s):  
Lynn J. Groome ◽  
Donna M. Mooney ◽  
Scherri B. Holland ◽  
Lisa A. Smith ◽  
Jana L. Atterbury ◽  
...  
Keyword(s):  
Time Series ◽  
Dynamic Models ◽  
Time Series Data ◽  
Low Risk ◽  
Series Data ◽  
Correlated Noise ◽  
Human Fetuses ◽  
Original Time Series ◽  
Uncorrelated Noise ◽  
Original Time

Approximate entropy (ApEn) is a statistic that quantifies regularity in time series data, and this parameter has several features that make it attractive for analyzing physiological systems. In this study, ApEn was used to detect nonlinearities in the heart rate (HR) patterns of 12 low-risk human fetuses between 38 and 40 wk of gestation. The fetal cardiac electrical signal was sampled at a rate of 1,024 Hz by using Ag-AgCl electrodes positioned across the mother’s abdomen, and fetal R waves were extracted by using adaptive signal processing techniques. To test for nonlinearity, ApEn for the original HR time series was compared with ApEn for three dynamic models: temporally uncorrelated noise, linearly correlated noise, and linearly correlated noise with nonlinear distortion. Each model had the same mean and SD in HR as the original time series, and one model also preserved the Fourier power spectrum. We estimated that noise accounted for 17.2–44.5% of the total between-fetus variance in ApEn. Nevertheless, ApEn for the original time series data still differed significantly from ApEn for the three dynamic models for both group comparisons and individual fetuses. We concluded that the HR time series, in low-risk human fetuses, could not be modeled as temporally uncorrelated noise, linearly correlated noise, or static filtering of linearly correlated noise.

scholarly journals Pulsation Properties of Hydrodynamic Models with Dimensional Analysis

1993 ◽  
Vol 139 ◽  
pp. 204-205
Author(s):  
Toshiki Aikawa
Keyword(s):  
Time Series ◽  
Light Curve ◽  
Dimensional Analysis ◽  
Dynamic Models ◽  
Time Series Data ◽  
Series Data ◽  
Hydrodynamic Models ◽  
Stellar Envelopes

AbstractWe demonstrate that the dimension deduced from time series data of hydro-dynamic models for chaotic pulsation is a function of luminosity. The dimension is proposed as a good quantity to guess stellar parameters and the physics of stellar envelopes like as the pulsation periods and light curve shapes used for regular variables.

scholarly journals ON MACHINE-LEARNED CLASSIFICATION OF VARIABLE STARS WITH SPARSE AND NOISY TIME-SERIES DATA

2011 ◽  
Vol 733 (1) ◽  
pp. 10 ◽  
Author(s):  
Joseph W. Richards ◽  
Dan L. Starr ◽  
Nathaniel R. Butler ◽  
Joshua S. Bloom ◽  
John M. Brewer ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Variable Stars ◽  
Series Data ◽  
Noisy Time Series
Sign in / Sign up

Export Citation Format

Share Document