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.

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 Multivariate Multiscale Dispersion Entropy of Biomedical Times Series

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 913 ◽  
Author(s):  
Hamed Azami ◽  
Alberto Fernández ◽  
Javier Escudero
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Channel Noise ◽  
Original Algorithm ◽  
Times Series ◽  
Non Linear ◽  
Long Time ◽  
Spatial Domains ◽  
The Stability ◽  
Multivariate Signals

Due to the non-linearity of numerous physiological recordings, non-linear analysis of multi-channel signals has been extensively used in biomedical engineering and neuroscience. Multivariate multiscale sample entropy (MSE–mvMSE) is a popular non-linear metric to quantify the irregularity of multi-channel time series. However, mvMSE has two main drawbacks: (1) the entropy values obtained by the original algorithm of mvMSE are either undefined or unreliable for short signals (300 sample points); and (2) the computation of mvMSE for signals with a large number of channels requires the storage of a huge number of elements. To deal with these problems and improve the stability of mvMSE, we introduce multivariate multiscale dispersion entropy (MDE–mvMDE), as an extension of our recently developed MDE, to quantify the complexity of multivariate time series. We assess mvMDE, in comparison with the state-of-the-art and most widespread multivariate approaches, namely, mvMSE and multivariate multiscale fuzzy entropy (mvMFE), on multi-channel noise signals, bivariate autoregressive processes, and three biomedical datasets. The results show that mvMDE takes into account dependencies in patterns across both the time and spatial domains. The mvMDE, mvMSE, and mvMFE methods are consistent in that they lead to similar conclusions about the underlying physiological conditions. However, the proposed mvMDE discriminates various physiological states of the biomedical recordings better than mvMSE and mvMFE. In addition, for both the short and long time series, the mvMDE-based results are noticeably more stable than the mvMSE- and mvMFE-based ones. For short multivariate time series, mvMDE, unlike mvMSE, does not result in undefined values. Furthermore, mvMDE is faster than mvMFE and mvMSE and also needs to store a considerably smaller number of elements. Due to its ability to detect different kinds of dynamics of multivariate signals, mvMDE has great potential to analyse various signals.

Sign in / Sign up

Export Citation Format

Share Document