Constant harvest policies for fish and wildlife populations can lead to population collapse in the face of stochastic variation in population growth rates. Here, we show that weak compensatory response by resource users or managers to changing levels of resource abundance can readily induce harvest cycles that accentuate the risk of catastrophic population collapse. Dynamic system models incorporating this mix of feedback predict that cycles or quasi-cycles with decadal periodicity should commonly occur in harvested wildlife populations, with effort and quotas lagging far behind resources, whereas harvests should exhibit lags of intermediate length. Empirical data gathered from three hunted populations of white-tailed deer and moose were consistent with these predictions of both underlying behavioral causes and dynamical consequences.

Summary1. Synchronous fluctuations of geographically separated populations are in general explained by the Moran effect, i.e. a common influence on the local population dynamics of environmental variables that are correlated in space. Empirical support for such a Moran effect has been difficult to provide, mainly due to problems separating out effects of local population dynamics, demographic stochasticity and dispersal that also influence the spatial scaling of population processes. Here we generalize the Moran effect by decomposing the spatial autocorrelation function for fluctuations in the size of great tit Parus major and blue tit Cyanistes caeruleus populations into components due to spatial correlations in the environmental noise, local differences in the strength of density regulation and the effects of demographic stochasticity. 2. Differences between localities in the strength of density dependence and nonlinearity in the density regulation had a small effect on population synchrony, whereas demographic stochasticity reduced the effects of the spatial correlation in environmental noise on the spatial correlations in population size by 21·7% and 23·3% in the great tit and blue tit, respectively. 3. Different environmental variables, such as beech mast and climate, induce a common environmental forcing on the dynamics of central European great and blue tit populations. This generates synchronous fluctuations in the size of populations located several hundred kilometres apart. 4. Although these environmental variables were autocorrelated over large areas, their contribution to the spatial synchrony in the population fluctuations differed, dependent on the spatial scaling of their effects on the local population dynamics. We also demonstrate that this effect can lead to the paradoxical result that a common environmental variable can induce spatial desynchronization of the population fluctuations.

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Many species with currently continuously distributed populations have histories of geographic range shifts and successive shifts between decline or fragmentation, growth and spatial expansion. The moose (Alces alces) colonised Scandinavia after the last ice age. Historic records document a high abundance and a wide distribution across Norway in the middle ages, but major decline and fragmentation in the eighteenth and nineteenth centuries. After growth and expansion during the twentieth century, the Norwegian population is currently abundant and continuously distributed. We examined the distribution of genetic variation, differentiation and admixture in Norwegian moose, using 15 microsatellites. We assessed whether admixture has homogenised the population or if there are any genetic structures or discontinuities that can be related to recent or ancient shifts in demography or distribution. The Bayesian clustering algorithm STRUCTURE without any spatial information showed that there is currently a genetic dichotomy dividing the population into one southern and one northern subpopulation. Including spatial information, the Bayesian clustering algorithm TESS, which considers gradients of genetic variation and spatial autocorrelation, suggests that the population is divided into three subpopulations along a latitudinal axis, the southern one identical to the one identified with STRUCTURE. Present convergence zones of high admixture separate the identified subpopulations, which are delimited by genetic discontinuities corresponding to geographic barriers against dispersal, e.g. wide fiords and mountain ranges. The distribution of the subpopulations is supported by spatial autocorrelation analysis. However, some loci are not in Hardy-Weinberg equilibrium and the STRUCTURE analysis suggests that a lower hierarchical structure may exist within the southernmost subpopulation. No bottlenecks or founder events are indicated by the levels of genetic variation, rather a high degree of private alleles in the northern subpopulations indicates introgression. Coalescent-based Approximate Bayesian Computation estimates unambiguously suggest that the genetic structure is a result of an ancient divergence event and a more recent admixture event a few centuries ago. This indicates that the central Scandinavian subpopulation constitutes a relatively recent convergence zone of secondary contact.

The relative importance of environmental colour for extinction risk compared with other aspects of environmental noise (mean and interannual variability) is poorly understood. Such knowledge is currently relevant, as climate change can cause the mean, variability and temporal autocorrelation of environmental variables to change. Here, we predict that the extinction risk of a shorebird population increases with the colour of a key environmental variable: winter temperature. However, the effect is weak compared with the impact of changes in the mean and interannual variability of temperature. Extinction risk was largely insensitive to noise colour, because demographic rates are poor in tracking the colour of the environment. We show that three mechanisms-which probably act in many species-can cause poor environmental tracking: (i) demographic rates that depend nonlinearly on environmental variables filter the noise colour, (ii) demographic rates typically depend on several environmental signals that do not change colour synchronously, and (iii) demographic stochasticity whitens the colour of demographic rates at low population size. We argue that the common practice of assuming perfect environmental tracking may result in overemphasizing the importance of noise colour for extinction risk. Consequently, ignoring environmental autocorrelation in population viability analysis could be less problematic than generally thought.