Predator-induced synchrony in population oscillations of coexisting small mammal species

Abstract

Comprehensive analyses of long-term (1977-2003) small-mammal abundance data from western Finland showed that populations of Microtus voles (field voles M. agrestis and sibling voles M. rossiaemeridionalis) voles, bank (Clethrionomys glareolus) and common shrews (Sorex araneus) fluctuated synchronously in 3 year population cycles. Time-series analyses indicated that interspecific synchrony is influenced strongly by density-dependent processes. Synchrony among Microtus and bank voles appeared additionally to be influenced by density-independent processes. To test whether interspecific synchronization through density-dependent processes is caused by predation, we experimentally reduced the densities of the main predators of small mammals in four large agricultural areas, and compared small mammal abundances in these to those in four control areas (2.5-3 km(2)) through a 3 year small-mammal population cycle. Predator reduction increased densities of the main prey species, Microtus voles, in all phases of the population cycle, while bank voles, the most important alternative prey of predators, responded positively only in the low and the increase phase. Manipulation also increased the autumn densities of water voles (Arvicola terrestris) in the increase phase of the cycle. No treatment effects were detected for common shrews or mice. Our results are in accordance with the alternative prey hypothesis, by which predators successively reduce the densities of both main and alternative prey species after the peak phase of small-mammal population cycles, thus inducing a synchronous low phase.

Figure 1

Long-term population fluctuations of (a) Microtus voles, (b) bank voles, (c) common shrews, (d) water voles and (e) pooled harvest and house mice in western Finland. Symbols denote ln-transformed values of autumn trapping indices (number of individuals caught per 100 trap nights) during 1977–2003 (1981–2003 for water voles). Original trapping data for common shrews were detrended by fitting a second-order polynomial curve.

Figure 2

(a) Autocorrelation functions (ACFs) for population time series of (i) Microtus voles, (ii) bank voles, (iii) common shrews, (iv) water voles and (v) pooled harvest and house mice in western Finland at time lags of 0–12 years. Stars above positive value bars indicate strong evidence for cyclic population fluctuations with a period length equal to the number of time lags on the x-axis. Stars below negative value bars indicate weak evidence for cyclic population fluctuations with a period length equal to approximately twice the number of time lags on the x-axis. (b) Partial rate correlation functions (PRCFs) for the same time-series at time lags of 1–6 years. Stars below the bars indicate the number of lags included as explanatory variables into autoregressive models for each species. Dashed horizontal lines in all plots indicate critical levels of significance for the correlations, obtained with Bartlett’s test.

Figure 3

The density index (measured as the mean number of individuals per trap line per area ±s.e.m., n=4) of (a) Microtus voles, (b) bank voles, (c) common shrews, (d) water voles and (e) pooled harvest and house mice from April to October (A, April; J, June; Au, August; O, October) 1997–1999 in predator reduction (filled symbols) and control areas (open symbols). Note the different scale of the y-axis in the different panels. Stars above the plots indicate significant interactions (p<0.05) between time and treatment (see table 3; statistics for Microtus are in Korpimäki et al. (2002)).