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Related Experiment Video

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Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

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An empirically based model for latitudinal gradient in vole population dynamics.

P Turchin¹, I Hanski

¹Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, Connecticut 06269-3042, USA.

The American Naturalist

|May 1, 1997

Summary

Predator type influences vole population cycles. Specialist predators drive oscillations in northern Europe, while generalist predators stabilize southern populations, explaining geographical patterns in vole dynamics.

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Area of Science:

Ecology
Zoology
Population Dynamics

Background:

Vole populations in northern Europe show a latitudinal gradient in dynamics, with oscillations at high latitudes and stability at lower latitudes.
A hypothesis suggests specialist predators (weasels) drive northern oscillations, while generalist predators regulate southern populations, leading to stability.

Purpose of the Study:

To test the generalist/specialist predation hypothesis explaining latitudinal shifts in vole population dynamics.
To develop and validate an empirically based model predicting these geographical patterns.

Main Methods:

Constructed an empirically based mathematical model of vole population dynamics.
Estimated model parameters using existing data.
Predicted the latitudinal pattern of vole population fluctuation amplitude and periodicity.

Main Results:

The model accurately predicted the observed latitudinal shifts in population fluctuation amplitude and periodicity.
Model predictions aligned with nonlinear time-series analysis, indicating a shift from stable to chaotic dynamics with increasing latitude.
The model successfully explained the geographical gradient in vole population stability.

Conclusions:

Predator type (specialist vs. generalist) is a key factor driving latitudinal variations in vole population dynamics.
The findings strongly support the proposed hypothesis regarding the roles of specialist and generalist predators.
The model provides a robust framework for understanding and predicting population dynamics across geographical gradients.