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...

The sampling-importance resampling (SIR) algorithm aims at drawing a random sample from a target distribution π. First, a sample is drawn from a proposal distribution "q", and then from this a smaller sample is drawn with sample probabilities proportional to the importance ratios π/"q". We propose here a simple adjustment of the sample probabilities and show that this gives faster convergence. The results indicate that our version converges better also for small sample sizes. The SIR algorithms are compared with the Metropolis-Hastings (MH) algorithm with independent proposals. Although MH converges asymptotically faster, the results indicate that our improved SIR version is better than MH for small sample sizes. We also establish a connection between the SIR algorithms and importance sampling with normalized weights. We show that the use of adjusted SIR sample probabilities as importance weights reduces the bias of the importance sampling estimate. Copyright 2003 Board of the Foundation of the Scandinavian Journal of Statistics..

The ability of pulse-echo measurements to resolve closely spaced reflectors is limited by the duration of the ultrasonic pulse. Resolution can be improved by deconvolution, but this often fails because frequency selective attenuation introduces unknown changes in the pulse shape. In this paper we propose a maximum a posteriori algorithm for simultaneous estimation of a time varying pulse and high-resolution deconvolution. A priori information is introduced to encourage estimates where the pulse varies only slowly and the reflectivity sequence is sparse. This adds sufficient regularization to the problem, and no further assumptions on the pulse such as minimum phase or a particular parametric form are needed. The joint pulse and reflectivity estimate are computed iteratively by alternating steps of pulse estimation and reflectivity estimation. The first step amounts to only a linear least squares fit. The second step is a difficult combinatorial optimization problem that we solve by a suboptimal but efficient search procedure. Due to the sparseness assumption, our approach is particularly suited for layered media containing a limited number of abrupt impedance changes. This is a situation of interest in many applications of nondestructive evaluation. Synthetic and real data results show that the algorithm works well.

The application of Monte Carlo techniques to Bayesian state estimation is discussed. A simple theory for the Monte Carlo uncertainty is given and recursive Monte Carlo filters for general non-linear systems constructed from basic considerations. The methods are applied to a non-linear pendulum with measurement saturation and to bearings-only target tracking. The parameters of the measurement noise are in the bearings example determined on-line as part of the state estimation. The state vector then becomes six-dimensional, but the problem is still handled in real time. There is scope for improvement. Filter performance hinges on certain probability density estimates running in parallel with the filters. Errors in the estimated densities lead to filter inaccuracies that must be compensated by raising the number of Monte Carlo samples. Better ways of estimating the densities may lower this number and enhance speed.

No abstract