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We studied the response of the barn owl annual productivity to the common vole population numbers and variability to test the effects of environmental stochasticity on their life histories. Current theory predicts that temporal environmental variability can affect long-term nonlinear responses (e.g., production of young) both positively and negatively, depending on the shape of the relationship between the response and environmental variables. At the level of the Czech Republic, we examined the shape of the relationship between the annual sum of fledglings (annual productivity) and vole numbers in both non-detrended and detrended data. At the districts’ level, we explored whether the degree of synchrony (measured by the correlation coefficient) and the strength of the productivity response increase (measured by the regression coefficient) in areas with higher vole population variability measured by the s-index. We found that the owls’ annual productivity increased linearly with vole numbers in the Czech Republic. Furthermore, based on district data, we also found that synchrony between dynamics in owls’ reproductive output and vole numbers increased with vole population variability. However, the strength of the response was not affected by the vole population variability. Additionally, we have shown that detrending remarkably increases the Taylor’s exponent b relating variance to mean in vole time series, thereby reversing the relationship between the coefficient of variation and the mean. This shift was not responsible for the increased synchrony with vole population variability. Instead, we suggest that higher synchrony could result from high food specialization of owls on the common vole in areas with highly fluctuating vole populations. [ABSTRACT FROM AUTHOR]

Most modern population dynamics analyses of time series use simple population indices for ecological inference. These indices, collected for many years for various agricultural pests or game animals, are generally believed not to distort systematically feedback estimates because the assumption of linearity to population size roughly holds. To assess the relative importance of this assumption, we examined the effect of nonlinearity in a burrow index for voles on feedback estimates obtained through autoregressive modeling. We show that the issue of linearity is of less importance to ecological inference because the feedback estimates are routinely obtained on a logarithmic scale. Transforming data to logs has a strong linearization effect, removing most of the nonlinearity observed on the original scale. We conclude that the statistical tools for ecological inference, such as autoregressive log-linear models, are sufficiently robust to the systematic error imposed by index nonlinearity and that indices are valuable sources of ecological information even in situations when the assumed linear functional forms to population size were not exactly validated. We suggest that for time series modelers, the issue of a large sampling variation in small 'noisy' populations is by far a more burning one than the systematic error due to index nonlinearity. [ABSTRACT FROM AUTHOR]

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