Population Cycles Inferences from Experimental, Modeling, and Time Series Approaches

2002 ◽  
Author(s):  
Xavier Lambin ◽  
Charles J. Krebs
Keyword(s):  
Time Series ◽  
Food Web ◽  
Case Studies ◽  
Community Dynamics ◽  
Field Experiments ◽  
Model Building ◽  
Time Series Data ◽  
Population Cycles ◽  
Research Traditions ◽  
Web Architecture

Some of the most interesting debates in population ecology have taken place within the context of population cycles. Their study has been a fertile ground for the development of ideas on how population models should be formulated and confronted with data. It is the setting in which the use of field experiments became established in ecology (e.g., Krebs and DeLong 1965), and also the context of many methodological and conceptual developments in the fields of population demography (Leslie and Ranson 1940), pest management (Berryman 1982), and community dynamics (Sinclair et al. 2000). Yet, as with many other issues in population dynamics, identifying without ambiguity the causes of population cycles in general, and for any organism in particular, continues to prove an extraordinarily difficult task. The major purpose of this book is to review recent research developments on the role of food web architecture, and more specifically on the effects of food, predators, and pathogens in population cycles. Its stated aim is to present evidence that population cycles could be caused by food web architecture in some natural systems. Whereas in chapter 1 Alan Berryman promotes a research program centered on the analysis of time series data for formulating, selecting, and even testing hypotheses on population cycles, the case studies encompass a much broader diversity of research approaches. The authors and coworkers of the seven case studies have combined time series analysis, model building, natural history observation, and experiments in different proportions to reach the conclusion that trophic interactions play an important role in generating cyclic dynamics. This diversity of approaches reflects, in part, a taxonomic divide between vertebrates and invertebrates, experiments being more common with the former, but also profound differences in research traditions. Indeed, the investment required to estimate population size and quantify the causes of mortality of moths and beetles is substantially less than that required for estimating the abundance of voles, hares, and grouse and their predators. From these practical constraints, divergent research traditions have evolved.

scholarly journals Uncertainty quantification of the effects of biotic interactions on community dynamics from nonlinear time-series data

2018 ◽  
Vol 15 (147) ◽  
pp. 20180695 ◽  
Author(s):  
Simone Cenci ◽  
Serguei Saavedra
Keyword(s):  
Time Series ◽  
Community Dynamics ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Model Averaging ◽  
Biotic Interactions ◽  
Series Data ◽  
Time Intervals ◽  
Novel Approach ◽  
Data Generating Process

Biotic interactions are expected to play a major role in shaping the dynamics of ecological systems. Yet, quantifying the effects of biotic interactions has been challenging due to a lack of appropriate methods to extract accurate measurements of interaction parameters from experimental data. One of the main limitations of existing methods is that the parameters inferred from noisy, sparsely sampled, nonlinear data are seldom uniquely identifiable. That is, many different parameters can be compatible with the same dataset and can generalize to independent data equally well. Hence, it is difficult to justify conclusive assertions about the effect of biotic interactions without information about their associated uncertainty. Here, we develop an ensemble method based on model averaging to quantify the uncertainty associated with the effect of biotic interactions on community dynamics from non-equilibrium ecological time-series data. Our method is able to detect the most informative time intervals for each biotic interaction within a multivariate time series and can be easily adapted to different regression schemes. Overall, this novel approach can be used to associate a time-dependent uncertainty with the effect of biotic interactions. Moreover, because we quantify uncertainty with minimal assumptions about the data-generating process, our approach can be applied to any data for which interactions among variables strongly affect the overall dynamics of the system.

Do Trophic Interactions Cause Population Cycles?

2002 ◽  
Author(s):  
Alan A. Berryman
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Extended Period ◽  
Population Cycles ◽  
Web Architecture ◽  
Animal Populations ◽  
Spruce Stands ◽  
Long Time ◽  
Exhaustive Study

My motivation in editing this book has been to present as compelling and credible a story as possible. Although I am personally convinced of the soundness of our argument, that food web architecture plays a key role in the cyclic dynamics of many animal populations, I am not sure that others will be so convinced. In this final chapter, therefore, I exercise my prerogative as editor to have the last word, a final attempt to convince the skeptics and to answer the critics.Perhaps the most compelling case comes from the Mikael Münster-Swendsen monumental study of a needleminer infesting Danish spruce forests (chapter 2). Mikael is the only person I know of who has, almost single-handedly, and with considerable precision, measured all the variables suspected of affecting the dynamics of a particular population over an extended period of time (19 years) and in several different localities (seven isolated spruce stands). Others have longer time series from more places, but none has been so complete in terms of the number of variables measured. This exhaustive study enabled him to build a model of the complete needleminer life system, and use this model to home in on the factors responsible for the cyclical dynamics. However, the story would not have been complete without multivariate time series analysis, which led to the discovery of parasitoids as the cause of the key feedback process, density-related reduction in fecundity. The lesson from Münster-Swendsen's work is clear: If we want to understand population dynamics, we need long time series for all the variables likely to affect the dynamics of the subject population(s). In other words, we need to consistently monitor ecological systems over long periods of time and in many different locations. If there is a weakness in his study, it is the absence of the final definitive experiment. Such an experiment would be relatively easy and cheap to do (relative to those described in other chapters), because isolated spruce stands are common in Denmark and parasitoids emerge from the soil a week or two after the needleminer. Thus, parasitoids could easily be excluded by spraying the ground with an insecticide after needleminer emergence.

Modeling Sales-Advertising Relationships: An Integrated Time Series–Econometric Approach

1983 ◽  
Vol 20 (3) ◽  
pp. 291-295 ◽  
Author(s):  
Robert P. Leone
Keyword(s):  
Time Series ◽  
Model Building ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Series Data ◽  
Response Models ◽  
Dynamic Relationship ◽  
Sales Response ◽  
Integrated Time Series ◽  
Econometric Approach

Since Palda's pioneering work investigating the dynamic relationship between sales and advertising, the marketing literature has contained many articles on the topic of sales response model building. Until recently, most of these articles have reported the construction of econometric models based on time series data. Recent applications of multivariate time series extensions of the work by Box and Jenkins have shown the usefulness of this methodology in building sales response models. The author discusses the distinctions between the econometric and time series approaches and, through a multivariate time series analysis, explores the competitive environment of an industry in which advertising is the main source of competition.

scholarly journals Inferring phage-bacteria infection networks from time series data

2016 ◽  
Author(s):  
Luis F. Jover ◽  
Justin Romberg ◽  
Joshua S. Weitz
Keyword(s):  
Time Series ◽  
Time Course ◽  
Community Dynamics ◽  
Time Series Data ◽  
Plaque Assay ◽  
Interaction Network ◽  
Series Data ◽  
Factors Affecting ◽  
Evolutionary Changes

In communities with bacterial viruses (phage) and bacteria, the phage-bacteria infection network establishes which virus types infects which host types. The structure of the infection network is a key element in understanding community dynamics. Yet, this infection network is often difficult to ascertain. Introduced over 60 years ago, the plaque assay remains the gold-standard for establishing who infects whom in a community. This culture-based approach does not scale to environmental samples with increased levels of phage and bacterial diversity, much of which is currently unculturable. Here, we propose an alternative method of inferring phage-bacteria infection networks. This method uses time series data of fluctuating population densities to estimate the complete interaction network without having to test each phage-bacteria pair individually. We use in silico experiments to analyze the factors affecting the quality of network reconstruction and find robust regimes where accurate reconstructions are possible. In addition, we present a multi-experiment approach where time series from different experiments are combined to improve estimates of the infection network and mitigate against the possibility of evolutionary changes to infection during the time-course of measurement.

Population Cycles of the Larch Budmoth in Switzerland

2002 ◽  
Author(s):  
Peter Turchin ◽  
Cheryl J. Briggs
Keyword(s):  
Time Series ◽  
Food Quality ◽  
Time Series Data ◽  
Population Cycles ◽  
Series Data ◽  
Food Quantity ◽  
The Status ◽  
The Rich ◽  
Rich Data

The population dynamics of the larch budmoth (LBM), Zeiraphera diniana, in the Swiss Alps are perhaps the best example of periodic oscillations in ecology (figure 7.1). These oscillations are characterized by a remarkably regular periodicity, and by an enormous range of densities experienced during a typical cycle (about 100,000-fold difference between peak and trough numbers). Furthermore, nonlinear time series analysis of LBM data (e.g., Turchin 1990, Turchin and Taylor 1992) indicates that LBM oscillations are definitely generated by a second-order dynamical process (in other words, there is a strong delayed density dependence—see also chapter 1). Analysis of time series data on LBM dynamics from five valleys in the Alps suggests that around 90% of variance in Rt is explained by the phenomenological time series model employing lagged LBM densities, R, =f(Ni-1,Ni-2,) (Turchin 2002). As discussed in the influential review by Baltensweiler and Fischlin (1988) about a decade ago, ecological theory suggests a number of candidate mechanisms that can produce the type of dynamics observed in the LBM (see also chapter 1). Baltensweiler and Fischlin concluded that changes in food quality induced by previous budmoth feeding was the most plausible explanation for the population cycles. During the last decade, the issue of larch budmoth oscillations was periodically revisited by various population ecologists looking for general insights about insect population cycles (e.g., Royama 1977, Bowers et al. 1993, Ginzburg and Taneyhill 1994, Den Boer and Reddingius 1996, Hunter and Dwyer 1998, Berryman 1999). These authors generally concurred with the view that budmoth cycles are driven by the interaction with food quality. A recent reanalysis of the rich data set on budmoth population ecology collected by Swiss researchers over a period of several decades, however, suggested that the role of parasitism is underappreciated (Turchin et al. 2002). Before focusing on the roles of food quality and parasitism in LBM dynamics, we briefly review the status of other hypotheses that were discussed in the literature on LBM cycles. First, the natural history of the LBM-larch system is such that food quantity is an unlikely factor to explain LBM oscillations.

scholarly journals Inferring phage–bacteria infection networks from time-series data

2016 ◽  
Vol 3 (11) ◽  
pp. 160654 ◽  
Author(s):  
Luis F. Jover ◽  
Justin Romberg ◽  
Joshua S. Weitz
Keyword(s):  
Time Series ◽  
Time Course ◽  
Community Dynamics ◽  
Time Series Data ◽  
Plaque Assay ◽  
Interaction Network ◽  
Series Data ◽  
Factors Affecting ◽  
Evolutionary Changes

In communities with bacterial viruses (phage) and bacteria, the phage–bacteria infection network establishes which virus types infect which host types. The structure of the infection network is a key element in understanding community dynamics. Yet, this infection network is often difficult to ascertain. Introduced over 60 years ago, the plaque assay remains the gold standard for establishing who infects whom in a community. This culture-based approach does not scale to environmental samples with increased levels of phage and bacterial diversity, much of which is currently unculturable. Here, we propose an alternative method of inferring phage–bacteria infection networks. This method uses time-series data of fluctuating population densities to estimate the complete interaction network without having to test each phage–bacteria pair individually. We use in silico experiments to analyse the factors affecting the quality of network reconstruction and find robust regimes where accurate reconstructions are possible. In addition, we present a multi-experiment approach where time series from different experiments are combined to improve estimates of the infection network. This approach also mitigates against the possibility of evolutionary changes to relevant phenotypes during the time course of measurement.

scholarly journals The Plankton Lifeform Extraction Tool: A digital tool to increase the discoverability and usability of plankton time-series data

2021 ◽  
Author(s):  
Clare Ostle ◽  
Kevin Paxman ◽  
Carolyn A. Graves ◽  
Mathew Arnold ◽  
Felipe Artigas ◽  
...  
Keyword(s):  
Time Series ◽  
Food Web ◽  
Large Scale ◽  
Time Series Data ◽  
Multiple Stressors ◽  
Marine Species ◽  
Series Data ◽  
Biological Traits ◽  
Digital Tool ◽  
North East

Abstract. Plankton form the base of the marine food web and are sensitive indicators of environmental change. Plankton time-seriesare therefore an essential part of monitoring progress towards global biodiversity goals, such as the Convention onBiological Diversity Aichi Targets, and for informing ecosystem-based policy, such as the EU Marine Strategy FrameworkDirective. Multiple plankton monitoring programmes exist in Europe, but differences in sampling and analysis methodsprevent the integration of their data, constraining their utility over large spatio-temporal scales. The Plankton LifeformExtraction Tool brings together disparate European plankton datasets into a central database from which it extractsabundance time-series of plankton functional groups, called ‘lifeforms’, according to shared biological traits. This tool hasbeen designed to make complex plankton datasets accessible and meaningful for policy, public interest, and scientificdiscovery. It allows examination of large-scale shifts in lifeform abundance or distribution (for example, holoplankton beingpartially replaced by meroplankton), providing clues to how the marine environment is changing. The lifeform methodenables datasets with different plankton sampling and taxonomic analysis methodologies to be used together to provideinsights into the response to multiple stressors and robust policy evidence for decision making. Lifeform time-seriesgenerated with the Plankton Lifeform Extraction Tool currently inform plankton and food web indicators for the UK’sMarine Strategy, the EU’s Marine Strategy Framework Directive, and for the Convention for the Protection of the MarineEnvironment of the North- East Atlantic (OSPAR) biodiversity assessments. The Plankton Lifeform Extraction Toolcurrently integrates 155,000 samples, containing over 44 million plankton records, from 9 different plankton datasets withinUK and European Seas, collected between 1924 and 2017. Additional datasets can be added, and time-series updated. ThePlankton Lifeform Extraction Tool is hosted by The Archive for Marine Species and Habitats Data (DASSH) athttps://www.dassh.ac.uk/lifeforms/. The lifeform outputs are linked to specific, doi-ed, versions of the Plankton LifeformTraits Master List and each underlying dataset.

scholarly journals Advanced Hybrid Machine Learning Algorithms for Multistep Lake Water Level Forecasting

2021 ◽  
Author(s):  
Elham Fijani ◽  
Khabat Khosravi ◽  
Rahim Barzegar ◽  
John Quilty ◽  
Jan Adamowski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Water Level ◽  
Lake Michigan ◽  
Model Building ◽  
Time Series Data ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Lake Water Level ◽  
Visually Based

Abstract Random Tree (RT) and Iterative Classifier Optimizer (ICO) based on Alternating Model Tree (AMT) regressor machine learning (ML) algorithms coupled with Bagging (BA) or Additive Regression (AR) hybrid algorithms were applied to forecasting multistep ahead (up to three months) Lake Superior and Lake Michigan water level (WL). Partial autocorrelation (PACF) of each lake’s WL time series estimated the most important lag times — up to five months in both lakes — as potential inputs. The WL time series data was partitioned into training (from 1918 to 1988) and testing (from 1989 to 2018) for model building and evaluation, respectively. Developed algorithms were validated through statistically and visually based metric using testing data. Although both hybrid ensemble algorithms improved individual ML algorithms’ performance, the BA algorithm outperformed the AR algorithm. As a novel model in forecasting problems, the ICO algorithm was shown to have great potential in generating robust multistep lake WL forecasts.

Case studies with nematodes from the individual to ecosystem level.

2021 ◽  
pp. 341-371
Author(s):  
Hendrik Fueser ◽  
Birgit Gansfort ◽  
Nabil Majdi ◽  
Janina Schenk ◽  
Walter Traunspurger
Keyword(s):  
Food Web ◽  
Case Studies ◽  
Field Experiments ◽  
Sediment Quality ◽  
Population Level ◽  
Nematode Species ◽  
Individual Level ◽  
Level 3 ◽  
Ecosystem Level ◽  
The Individual

Abstract Organisms smaller than 2 mm in size are ideal candidates for laboratory and field experiments with a theoretical focus. This chapter illustrates this point by drawing on recently published works in which studies of nematodes have informed theories within population and community ecology. Case studies examining the following are presented: (1) Life cycle experiments (individual level), (2) The interactions of two nematode species - competition experiments (population level), (3) Nematode community-based assessments of sediment quality (community level), (4) Nematodes in a detritus-based food web model (food web level).

scholarly journals Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality

Genes ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 216 ◽  
Author(s):  
Dongmei Ai ◽  
Xiaoxin Li ◽  
Gang Liu ◽  
Xiaoyi Liang ◽  
Li Xia
Keyword(s):  
Time Series ◽  
Microbial Community ◽  
Granger Causality ◽  
Large Scale ◽  
Community Dynamics ◽  
Time Series Data ◽  
Series Data ◽  
Association Network ◽  
Causal Relationships ◽  
Network Analyses

The increasing availability of large-scale time series data allows the inference of microbial community dynamics by association network analysis. However, correlation-based association network analyses are noninformative of causal, mediating and time-dependent relationships between microbial community functional factors. To address this insufficiency, we introduced the Granger causality model to the analysis of a recent marine microbial time series dataset. We systematically constructed a directed acyclic network, representing both internal and external causal relationships among the microbial and environmental factors. We further optimized the network by removing false causal associations using the conditional Granger causality. The final network was visualized as a Granger graph, which was analyzed to identify causal relationships driven by key functional operators in the environment, such as Gammaproteobacteria, which was Granger caused by total organic nitrogen and primary production (p < 0.05 and Q < 0.05).

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