Seasonality, density dependence, and population cycles in Hokkaido voles

Abstract

Voles and lemmings show extensive variation in population dynamics regulated across and within species. In an attempt to develop and test generic hypotheses explaining these differences, we studied 84 populations of the gray-sided vole (Clethrionomys rufocanus) in Hokkaido, Japan. We show that these populations are limited by a combination of density-independent factors (such as climate) and density-dependent processes (such as specialist predators). We show that density-dependent regulation primarily occurs in winter months, so that populations experiencing longer winters tend to have a stronger delayed density-dependence and, as a result, exhibit regular density cycles. Altogether, we demonstrate that seasonality plays a key role in determining whether a vole population is cyclic or not.Clethrionomys rufocanus ͉ seasonal and annual density dependence ͉ state-space modeling ͉ sampling variance A long controversy over the issue of density-dependent versus -independent population regulation has led to the conclusion that both factors are important for understanding population fluctuations (1-8). It is, however, less clear how such density-dependent and -independent factors interact with each other in shaping the dynamic pattern of populations across a larger part of the species range. In an attempt to disentangle these issues, we analyze a set of 30-year seasonal (spring and fall) time series from 84 populations of the gray-sided vole [Clethrionomys rufocanus (Sundevall, 1846)] from Hokkaido, Japan (Fig. 1A) (9, 10). To investigate the role of seasonality in the generation of population cycles, we decompose the annual (fall-to-fall) density dependence, as well as the densityindependent stochasticity into their seasonal components. The added detail provided by pinpointing the seasonal arena of population regulation (see supporting information on the PNAS web site, www.pnas.org) provides us with a better basis for suggesting and evaluating hypotheses about the biological mechanisms that cause density dependence, stochasticity, and population f luctuations.A perennial problem in the study of population dynamics has been the relative lack of extensive and accurate data. In this study we attempt to reach more accurate conclusions than are usually possible by two means. First, we use comparative time-series data from a large number of very similar populations. Second, we address the very substantial problem of biased and imprecise measures of population density through use of a state-space modeling approach (11-13), where time-series observations are related to unobserved ''states'' of the real population through a probabilistic observation model accounting for sampling variation. Our study confirms and extends an earlier study of ours (14); whereas the earlier study used only fall data (and as a result could cover the entire island of Hokkaido), the present study used both fall and spring data. The greater detail of the data used in this paper makes a much more detailed analysis of the seasonal structure possible; a p...

Experimental evidence that livestock grazing intensity affects cyclic vole population regulation processes

“…Though this technique did not allow us to pinpoint the exact causal chain that generated a change in vole dynamics, the combination of experimental approach (including manipulation and replication, and control of all other factors by virtue of experimental randomisation) and time series analysis allowed us to identify delayed density dependence as the regulatory process affected by livestock grazing with little evidence of direct density dependence being affected. As such, combining time series analysis with a factorial experiment is a methodological step forward relative to previous attempts to link density dependent parameters (probes) to unmanipulated environmental covariates, such as latitude and seasonality (e.g., Bjørnstad et al 1995;Erb et al 2000;Saitoh et al 2003). Another relevant aspect of the method is that, despite time series being relatively short (8 years), the combination of a randomized, replicated large-scale experimental set up with a state-space analytical model was essential to obtain estimates of probes with greater precision and less bias than hitherto feasible (e.g., Hansen et al 1999;Stenseth et al 2003).…”

“…As such, combining time series analysis with a factorial experiment is a methodological step forward relative to previous attempts to link density dependent parameters (probes) to unmanipulated environmental covariates, such as latitude and seasonality (e.g., Bjørnstad et al 1995;Erb et al 2000;Saitoh et al 2003). Another relevant aspect of the method is that, despite time series being relatively short (8 years), the combination of a randomized, replicated large-scale experimental set up with a state-space analytical model was essential to obtain estimates of probes with greater precision and less bias than hitherto feasible (e.g., Hansen et al 1999;Stenseth et al 2003).…”

“…Observation error may cause bias in the estimation of densitydependence. A Bayesian state-space model can be used to account for observation error by combining the system process with an observation process that describes the statistical relationship between the observation and the unknown true density that would be measured under infinite sampling effort (Stenseth et al 2003). …”

“…However, much of the supportive evidence derives from time-series approaches, and hence suffers from its correlative and retrospective nature (Lambin et al 2002). For instance, interpretations of geographical gradients in the density dependence structure of vole population cycles are a high profile case where multiple plausible explanations might fit the same observed pattern (e.g., Bjørnstad et al 1995;Erb et al 2000;Saitoh et al 2003). Thus, arguably, causal interpretation of variations in the density dependence structure observed over time or space are, necessarily, ambiguous in the absence of experimental validation (Stenseth 1999).…”

Effects of acorn masting on population dynamics of three forest‐dwelling rodent species in Hokkaido, Japan

“…Previously (Stenseth et al 2003 Stenseth et al 2003). Here, we used the minimum number alive (Krebs 1999) as rodent abundance and the number of trap-nights as trapping effort.…”