Use of coupled oscillator models to understand synchrony and travelling waves in populations of the field vole Microtus agrestis in northern England

1. Earlier studies have reported that field vole Microtus agrestis populations in Kielder Forest, UK, exhibit typical 3–4-year cyclical dynamics, and that the observed spatiotemporal patterns are consistent with a travelling wave in vole abundance moving along an axis south-west–north-east at approximately 19 km year–1. One property of this wave is that nearby populations fluctuate more synchronously than distant ones, with correlations falling lower than the average for the sampling area beyond approximately 13 km. 2. In this paper we present a series of models that investigate the possibility that both the observed degree of synchrony and the travelling wave can be explained as a simple consequence of linking a series of otherwise independently oscillating populations. Our ‘coupled oscillator’ models consider a series of populations, distributed either in a linear array or in a two-dimensional regular matrix. Local population fluctuations, each with a 3–4-year period, were generated using either a Ricker equation or a set of discrete-time Lotka–Volterra equations. Movement among populations was simulated either by a fixed proportion of each population moving locally to their nearest neighbour populations, or the same proportion being distributed via a continuous geometric function (more distant populations receiving less). 3. For a variety of different ways of generating cycles and a number of different movement rules, local exchange between oscillating populations tended to generate synchrony domains that extended over a large number of populations. When the rates of exchange between local populations were relatively low, then permanent travelling waves emerged, especially after an initial invasion phase. There was a non-linear relationship between the amount of dispersal and the domain of synchrony that this movement generated. Furthermore, the observed spatiotemporal patterns that emerged following an initial invasion phase were found to be highly dependent on the extreme distances reached by rare dispersers. 4. As populations of voles are predominantly distributed in grassland patches created by clear-cutting of forest stands, we estimated the mean patch diameter and mean interpatch distance using a geographical information system (GIS) of the forest. Our simplified models suggest that if as much as 5–10% of each vole population dispersed a mean of 178 m between clear-cuts per generation, then this would generate a synchrony domain and speed of wave in the region of 6–24 km (per year), which is reasonably consistent with the observed synchrony domain and speed. Much less dispersal would be capable of generating this scale of domain if some individuals occasionally moved beyond the nearest-neighbour patch. 5. While we still do not know what causes the local oscillations, our models question the need to invoke additional factors to explain large-scale synchrony and travelling waves beyond small-scale dispersal and local density-dependent feedback. Our work also suggests that the higher degrees of synchrony observed in Fennoscandian habitats compared with Kielder may be due in part to the relative ease of movement of voles in these former habitats. As our work confirms that the rates of exchange among local populations will have a strong influence on synchrony, then we anticipate that the spatiotemporal distribution of clear-cuts will also have an important influence on the dynamics of predators of voles.

[1]  A L Lloyd,et al.  Spatial heterogeneity in epidemic models. , 1996, Journal of theoretical biology.

[2]  J. Murray,et al.  On the spatial spread of rabies among foxes , 1986, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[3]  Jonathan A. Sherratt,et al.  Oscillations and chaos behind predator–prey invasion: mathematical artifact or ecological reality? , 1997 .

[4]  Veijo Kaitala,et al.  Travelling waves in vole population dynamics , 1997, Nature.

[5]  R. May Population biology: The voles of Hokkaido , 1998, Nature.

[6]  X. Lambin,et al.  Cyclic dynamics in field vole populations and generalist predation , 2000 .

[7]  Kaitala,et al.  Travelling wave dynamics and self-organization in a spatio-temporally structured population , 1998 .

[8]  J. Bascompte,et al.  Rethinking complexity: modelling spatiotemporal dynamics in ecology. , 1995, Trends in ecology & evolution.

[9]  S. J. Petty Ecology of the tawny owl Strix aluco in the spruce forests of Northumberland and Argyll , 1992 .

[10]  B. Bolker,et al.  Impact of vaccination on the spatial correlation and persistence of measles dynamics. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[11]  T. Clutton‐Brock,et al.  Noise and determinism in synchronized sheep dynamics , 1998, Nature.

[12]  Takashi Saitoh,et al.  SYNCHRONY AND SCALING IN DYNAMICS OF VOLES AND MICE IN NORTHERN JAPAN , 1999 .

[13]  P. A. P. Moran,et al.  The statistical analysis of the Canadian Lynx cycle. , 1953 .

[14]  Rolf A. Ims,et al.  Spatial and Temporal Patterns of Small‐Rodent Population Dynamics at a Regional Scale , 1996 .

[15]  Paul A. Racey,et al.  Large‐scale processes in ecology and hydrology , 2000 .

[16]  D Mollison,et al.  Dependence of epidemic and population velocities on basic parameters. , 1991, Mathematical biosciences.

[17]  David Gutiérrez,et al.  Habitat‐based statistical models for predicting the spatial distribution of butterflies and day‐flying moths in a fragmented landscape , 2000 .

[18]  David A. Elston,et al.  Spatial asynchrony and periodic travelling waves in cyclic populations of field voles , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[19]  G. Ruxton Synchronisation Between Individuals and the Dynamics of Linked Populations , 1996 .

[20]  W. Sutherland,et al.  Consequences of large‐scale processes for the conservation of bird populations , 2000 .

[21]  Paul C. Jepson,et al.  A metapopulation approach to modelling the long-term impact of pesticides on invertebrates. , 1993 .

[22]  Michael P. Hassell,et al.  Spatial structure and chaos in insect population dynamics , 1991, Nature.

[23]  I. Cattadori,et al.  The Moran effect: a cause of population synchrony. , 1999, Trends in ecology & evolution.

[24]  Richard A. Wadsworth,et al.  Simulating the spread and management of alien riparian weeds: are they out of control? , 2000 .

[25]  William Gurney,et al.  Circles and spirals: population persistence in a spatially explicit predator-prey model , 1998 .

[26]  Steven R. Dunbar,et al.  Travelling wave solutions of diffusive Lotka-Volterra equations , 1983 .

[27]  E. Ranta,et al.  The spatial dimension in population fluctuations , 1997, Science.

[28]  C. Thomas,et al.  Spatial synchrony in field vole Microtus agrestis abundance in a coniferous forest in northern England: The role of vole-eating raptors , 2000 .

[29]  Alasdair I. Houston,et al.  Spatially explicit, individual-based, behavioural models of the annual cycle of two migratory goose populations , 2000 .

[30]  Jan Lindström,et al.  Synchrony in population dynamics , 1995, Proceedings of the Royal Society of London. Series B: Biological Sciences.