Complex Population Dynamics in the Real World: Modeling the Influence of Time-Varying Parameters and Time Lags

Zeng, Zheng; Nowierski, Robert M.; Taper, Mark L.; Dennis, B. R.; Kemp, William P.

doi:10.2307/176721

Cited by 27 publications

(32 citation statements)

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“…Density regulation is an important concept in population dynamics and the problems of detecting it from short time series has been discussed by several authors (Bulmer 1975, Zeng et al 1998). In particular, if the density regulation is weak, the data will contain little information about the carrying capacity (Lande et al 2002) so that estimating carrying capacity and detecting density regulation or accurately estimating it will be difficult.…”

Section: Discussionmentioning

confidence: 99%

Bootstrap methods for estimating spatial synchrony of fluctuating populations

Lillegård

Engen²,

Sæther³

2005

Oikos

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E. 2005. Bootstrap methods for estimating spatial synchrony of fluctuating populations. Á/ Oikos 109: 342 Á/350.We describe and examine methods for estimating spatial correlations used in population ecology. We base our analyses on a hypothetical example of a species that has been censured at 30 different locations for 20 years. We assume that the population fluctuations can be described by a simple linear model on logarithmic scale. Stochastic simulations is utilized to check how seven different ways of resampling perform when the goal is to find nominal 95% confidence intervals for the spatial correlation in growth rates at given distances. It turns out that resampling of locations performs badly, with true coverage level as low as 30 Á/40%, especially for small correlations at long distances. Resampling of timepoints performs much better, with coverage varying from 80 to 90%, depending on the strength of density regulation and whether the spatial correlation is estimated for the response variable or for the error terms in the model. Assuming that the underlying model is known, the best results are achieved for parametric bootstrapping, a result that strongly emphasize the importance of defining and estimating a proper population model when studying spatial processes.Synchronous fluctuations in size of populations that are geographically separated from each other have now been demonstrated in many taxa, including birds (Ranta et al.

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Section: Discussionmentioning

confidence: 99%

Bootstrap methods for estimating spatial synchrony of fluctuating populations

Lillegård

Engen²,

Sæther³

2005

Oikos

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“…1987; Den Boer & Reddingius 1989; Reddingius & Den Boer 1989), most recent literature has assumed density dependence exists and focused on estimating its strength. This requires specifying a statistical model or suite of statistical models that are fit to time‐series data (Zeng et al. 1998; Osenberg et al.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, studying population regulation must involve model selection from a broad range of models that encompass the possible dynamics observed in the data (Shenk et al. 1998; Zeng et al. 1998; Kendall et al.…”

Section: Introductionmentioning

confidence: 99%

Weak population regulation in ecological time series

2009

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How strongly natural populations are regulated has a long history of debate in ecology. Here, we discuss concepts of population regulation appropriate for stochastic population dynamics. We then analyse two large collections of data sets with autoregressive-moving average (ARMA) models, using model selection techniques to find best-fitting models. We estimated two metrics of population regulation: the characteristic return rate of populations to stationarity and the variability of the stationary distribution (the long-term distribution of population abundance). Empirically, longer time series were more likely to show weakly regulated population dynamics. For data sets of length > or = 20, more than 35% had characteristic return times > 6 years, and more than 29% had stationary distributions whose coefficients of variation were more than two times greater than would be the case if they were maximally regulated. These results suggest that many natural populations are weakly regulated.

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“…However, the possible influence of sampling errors on these results remains unknown. Zeng et al (1998) analysed 31 data sets from different taxa, including two 31‐yr dabbling duck time series, the mallard and the gadwall ( Anas strepera ), from North America. They fitted several models to each time series and used Schwarz's information criterion to select the best model in each case.…”

Section: Discussionmentioning

confidence: 99%

Density dependence, regulation and open‐closed populations: insights from the wigeon,Anas penelope

Pöysä¹,

Pesonen²

2003

Oikos

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We investigated whether the degree of exchange with other populations affects the occurrence of density‐dependent regulation. We contrasted data from an Icelandic and a Finnish population of breeding wigeons (Anas penelope), the former population being more closed than the later. We looked for density dependence in time‐series data and investigated whether breeding success is density dependent and plays a role in population dynamics and regulation. Time‐series analysis did not reveal density‐dependent regulation in either population. Nor did we find evidence of density‐dependent breeding success in either population. However, population growth rate appeared to be strongly dependent on the breeding success in the previous year in the closed population but not in the open population. Our findings underline how important it is to link time‐series analysis to the study of potential stabilizing mechanisms in order to understand population dynamics and regulation. We also suggest that it may be a difficult task to achieve sustainability in waterfowl harvesting, the theoretical basis of which is density‐dependent population regulation.

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Complex Population Dynamics in the Real World: Modeling the Influence of Time-Varying Parameters and Time Lags

Cited by 27 publications

References 0 publications

Bootstrap methods for estimating spatial synchrony of fluctuating populations

Bootstrap methods for estimating spatial synchrony of fluctuating populations

Weak population regulation in ecological time series

Density dependence, regulation and open‐closed populations: insights from the wigeon,Anas penelope

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