Using Time Series to Estimate Rates of Population Change from Abundance Data

2007 ◽  
Vol 71 (1) ◽  
pp. 202-207 ◽  
Author(s):  
KRISTEN E. RYDING ◽  
JOSHUA J. MILLSPAUGH ◽  
JOHN R. SKALSKI
Keyword(s):  
Time Series ◽  
Population Change ◽  
Abundance Data

scholarly journals Characterization of Pathogen Airborne Inoculum Density by Information Theoretic Analysis of Spore Trap Time Series Data

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1343 ◽  
Author(s):  
Robin A. Choudhury ◽  
Neil McRoberts
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Complex Dynamics ◽  
Cost Effective ◽  
Series Data ◽  
Spore Trap ◽  
Abundance Data ◽  
Airborne Inoculum ◽  
Pathogen Dna ◽  
Low Dimensional

In a previous study, air sampling using vortex air samplers combined with species-specific amplification of pathogen DNA was carried out over two years in four or five locations in the Salinas Valley of California. The resulting time series data for the abundance of pathogen DNA trapped per day displayed complex dynamics with features of both deterministic (chaotic) and stochastic uncertainty. Methods of nonlinear time series analysis developed for the reconstruction of low dimensional attractors provided new insights into the complexity of pathogen abundance data. In particular, the analyses suggested that the length of time series data that it is practical or cost-effective to collect may limit the ability to definitively classify the uncertainty in the data. Over the two years of the study, five location/year combinations were classified as having stochastic linear dynamics and four were not. Calculation of entropy values for either the number of pathogen DNA copies or for a binary string indicating whether the pathogen abundance data were increasing revealed (1) some robust differences in the dynamics between seasons that were not obvious in the time series data themselves and (2) that the series were almost all at their theoretical maximum entropy value when considered from the simple perspective of whether instantaneous change along the sequence was positive.

scholarly journals Modifiable reporting unit problems and time series of long-term human activity

2020 ◽  
Vol 376 (1816) ◽  
pp. 20190726 ◽  
Author(s):  
A. Bevan ◽  
E. R. Crema
Keyword(s):  
Time Series ◽  
Human Activity ◽  
Real World ◽  
Population Change ◽  
Theme Issue ◽  
The Impact

This paper responds to a resurgence of interest in constructing long-term time proxies of human activity, especially but not limited to models of population change over the Pleistocene and/or Holocene. While very much agreeing with the need for this increased attention, we emphasize three important issues that can all be thought of as modifiable reporting unit problems: the impact of (i) archaeological periodization, (ii) uneven event durations and (iii) geographical nucleation-dispersal phenomena. Drawing inspiration from real-world examples from prehistoric Britain, Greece and Japan, we explore their consequences and possible mitigation via a reproducible set of tactical simulations. This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography’.

scholarly journals NetGAM: Using generalized additive models to improve the predictive power of ecological network analyses constructed using time-series data

2021 ◽  
Author(s):  
Samantha J Gleich ◽  
Jacob A Cram ◽  
Jake L Weissman ◽  
David A Caron
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Species Abundance ◽  
Covariance Structure ◽  
Additive Models ◽  
Series Data ◽  
Ecological Network ◽  
Network Analyses ◽  
Abundance Data ◽  
Species Abundance Data

Ecological network analyses are used to identify potential biotic interactions between microorganisms from species abundance data. These analyses are often carried out using time-series data; however, time-series networks have unique statistical challenges. Time-dependent species abundance data can lead to species co-occurrence patterns that are not a result of direct, biotic associations and may therefore result in inaccurate network predictions. Here, we describe a generalize additive model (GAM)-based data transformation that removes time-series signals from species abundance data prior to running network analyses. Validation of the transformation was carried out by generating mock, time-series datasets, with an underlying covariance structure, running network analyses on these datasets with and without our GAM transformation, and comparing the network outputs to the known covariance structure of the simulated data. The results revealed that seasonal abundance patterns substantially decreased the accuracy of the inferred networks. Additionally, the GAM transformation increased the F1 score of inferred ecological networks on average and improved the ability of network inference methods to capture important features of network structure. This study underscores the importance of considering temporal features when carrying out network analyses and describes a simple, effective tool that can be used to improve results.

Detection of Environmental Influence on Recruitment Using Abundance Data

1988 ◽  
Vol 45 (8) ◽  
pp. 1448-1458 ◽  
Author(s):  
Robert G. Kope ◽  
Louis W. Botsford
Keyword(s):  
Time Series ◽  
Oncorhynchus Tshawytscha ◽  
Environmental Influence ◽  
Simulated Data ◽  
Correlation Coefficients ◽  
Type I ◽  
Type Ii ◽  
Abundance Data ◽  
Sample Correlation ◽  
Type Ii Errors

Environmental influences on recruitment are usually detected by correlation analysis using environmental and recruitment data. When recruitment data are unavailable, abundance data are sometimes used instead. An abundance time series can be described as a convolution of the recruitment time series, and consequently, when it is used as a proxy for recruitment, the magnitude of correlations is lower and the probability of both type I and type II errors is greater. Two approaches can restore the correlation between the environmental series and the recruitment series: (1) convolution of the environmental series and (2) deconvolution of the abundance series. Convolution of the environmental series increases the variance of computed correlation coefficients and can further increase the probability of type II errors. Deconvolution of the abundance series can recover the correlation between the environment and recruitment without increasing the variance of sample correlation coefficients or the probability of type II errors, as long as the deconvolution is stable and errors in the data are relatively small. When errors in the data are sufficiently large or the deconvolution is sufficiently unstable, the best results may be obtained by simply using abundance as a recruitment index. We evaluate application of the deconvolution technique to chinook salmon (Oncorhynchus tshawytscha) catch and spawner abundance series using both real and simulated data.

scholarly journals Culture Process and the Interpretation of Radiocarbon Data

Radiocarbon ◽  
2017 ◽  
Vol 60 (2) ◽  
pp. 453-467 ◽  
Author(s):  
Jacob Freeman ◽  
David A Byers ◽  
Erick Robinson ◽  
Robert L Kelly
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Population Size ◽  
Population Change ◽  
Future Research ◽  
Human Populations ◽  
Data Sets ◽  
Economic Complexity ◽  
Using Data ◽  
Outstanding Question

AbstractOver the last decade, archaeologists have turned to large radiocarbon (14C) data sets to infer prehistoric population size and change. An outstanding question concerns just how direct of an estimate 14C dates are for human populations. In this paper we propose that 14C dates are a better estimate of energy consumption, rather than an unmediated, proportional estimate of population size. We use a parametric model to describe the relationship between population size, economic complexity and energy consumption in human societies, and then parametrize the model using data from modern contexts. Our results suggest that energy consumption scales sub-linearly with population size, which means that the analysis of a large 14C time-series has the potential to misestimate rates of population change and absolute population size. Energy consumption is also an exponential function of economic complexity. Thus, the 14C record could change semi-independent of population as complexity grows or declines. Scaling models are an important tool for stimulating future research to tease apart the different effects of population and social complexity on energy consumption, and explain variation in the forms of 14C date time-series in different regions.

scholarly journals Structural identifiability of the generalized Lotka–Volterra model for microbiome studies

2021 ◽  
Vol 8 (7) ◽  
pp. 201378
Author(s):  
Christopher H. Remien ◽  
Mariah J. Eckwright ◽  
Benjamin J. Ridenhour
Keyword(s):  
Time Series ◽  
Relative Abundance ◽  
Population Dynamic ◽  
Dynamic Models ◽  
Microbial Interactions ◽  
Volterra Model ◽  
Underlying Structure ◽  
Structural Identifiability ◽  
Abundance Data ◽  
Relative Abundances

Population dynamic models can be used in conjunction with time series of species abundances to infer interactions. Understanding microbial interactions is a prerequisite for numerous goals in microbiome research, including predicting how populations change over time, determining how manipulations of microbiomes affect dynamics and designing synthetic microbiomes to perform tasks. As such, there is great interest in adapting population dynamic theory for microbial systems. Despite the appeal, numerous hurdles exist. One hurdle is that the data commonly obtained from DNA sequencing yield estimates of relative abundances, while population dynamic models such as the generalized Lotka–Volterra model track absolute abundances or densities. It is not clear whether relative abundance data alone can be used to infer parameters of population dynamic models such as the Lotka–Volterra model. We used structural identifiability analyses to determine the extent to which a time series of relative abundances can be used to parametrize the generalized Lotka–Volterra model. We found that only with absolute abundance data to accompany relative abundance estimates from sequencing can all parameters be uniquely identified. However, relative abundance data alone do contain information on relative interaction strengths, which is sufficient for many studies where the goal is to estimate key interactions and their effects on dynamics. Using synthetic data of a simple community for which we know the underlying structure, local practical identifiability analysis showed that modest amounts of both process and measurement error do not fundamentally affect these identifiability properties.

scholarly journals Parameter Identifiability of the Generalized Lotka-Volterra Model for Microbiome Studies

2018 ◽  
Author(s):  
Christopher H Remien ◽  
Mariah J Eckwright ◽  
Benjamin J Ridenhour
Keyword(s):  
Time Series ◽  
Relative Abundance ◽  
Population Dynamic ◽  
Dynamic Models ◽  
Volterra Model ◽  
Sufficient Information ◽  
Absolute Abundance ◽  
Abundance Data ◽  
Relative Abundances ◽  
Over Time

AbstractBackgroundPopulation dynamic models can be used in conjunction with time series of species abundances to infer interactions. Understanding microbial interactions is a prerequisite for numerous goals in microbiome research; predicting how populations change over time, determining how manipulations of microbiomes affect dynamics, and designing synthetic microbiomes to perform tasks are just a few examples. As such, there is great interest in adapting population dynamic theory for microbial systems. Despite the appeal, numerous hurdles exist. One hurdle is that the data commonly obtained from DNA sequencing yield estimates of relative abundances, while population dynamic models such as the generalized Lotka-Volterra model track absolute abundances or densities. It is not clear whether relative abundance data alone can be used to infer parameters of population dynamic models such as the Lotka-Volterra model.ResultsWe used structural identifiability analyses to determine the extent to which time series of relative abundances can be used to parameterize the generalized Lotka-Volterra model. We found that only with absolute abundance data to accompany relative abundance estimates from sequencing can all parameters be uniquely identified. However, relative abundance data alone do contain information on relative interaction strengths, which is sufficient for many studies where the goal is to estimate key interactions and their effects on dynamics. Our results also indicate that the relative interaction rates that can be estimated using relative abundance data provide ample information to estimate relative changes of absolute abundance over time. Using synthetic data for which we know the underlying structure, we found our results to be robust to modest amounts of both process and measurement error.ConclusionsFitting the generalized Lotka-Volterra model to time-series sequencing data typically requires either assuming a constant population size or performing additional measurements to obtain absolute abundances. We have found that these assumptions are not strictly necessary because relative abundance data alone contain sufficient information to estimate relative rates of interaction, and thus to infer key drivers of microbial population dynamics.

scholarly journals Effects of weather and climate on the dynamics of animal population time series

2010 ◽  
Vol 278 (1708) ◽  
pp. 985-992 ◽  
Author(s):  
Jonas Knape ◽  
Perry de Valpine
Keyword(s):  
Time Series ◽  
Population Dynamics ◽  
Population Change ◽  
A Priori ◽  
Randomization Test ◽  
Weather Data ◽  
Climate Index ◽  
Climate Indices ◽  
Animal Populations ◽  
Weather And Climate

Weather is one of the most basic factors impacting animal populations, but the typical strength of such impacts on population dynamics is unknown. We incorporate weather and climate index data into analysis of 492 time series of mammals, birds and insects from the global population dynamics database. A conundrum is that a multitude of weather data may a priori be considered potentially important and hence present a risk of statistical over-fitting. We find that model selection or averaging alone could spuriously indicate that weather provides strong improvements to short-term population prediction accuracy. However, a block randomization test reveals that most improvements result from over-fitting. Weather and climate variables do, in general, improve predictions, but improvements were barely detectable despite the large number of datasets considered. Climate indices such as North Atlantic Oscillation are not better predictors of population change than local weather variables. Insect time series are typically less predictable than bird or mammal time series, although all taxonomic classes display low predictability. Our results are in line with the view that population dynamics is often too complex to allow resolving mechanisms from time series, but we argue that time series analysis can still be useful for estimating net environmental effects.

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