The distribution of noradrenergic neurons in the brain of the three-spined stickleback was demonstrated with the indirect peroxidase-antiperoxidase (PAP) immunohistochemical method with antibodies against a noradrenaline-bovine serum albumin conjugate. Noradrenergic neuronal somata were exclusively located in the isthmal area of the brain stem and in the lower medulla. Noradrenergic varicose axons innervate the reticular formation, motor nuclei, and interpeduncular nucleus of the brain stem, the hypothalamus and habenular nuclei, various parts of the area dorsalis telencephali (forebrain pallium), and the olfactory bulbs. Scattered noradrenergic axons were observed in the optic tectum and in various parts of the cerebellum. It is concluded that the isthmal cell group of the stickleback is, on topological and cytoarchitectonic grounds, equivalent to the ventral portion of the locus coeruleus/subcoeruleus area of amniotes, but that its efferent connections display features characteristic both of those originating in the locus coeruleus, and in the lateral tegmental cell groups of mammals.

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Abstract. Proxy records from climate archives provide evidence about past climate changes, but the recorded signal is affected by non-climate related effects as well as time uncertainty. As proxy based climate reconstructions are frequently used to test climate models and to quantitatively infer past climate, we need to improve our understanding of the proxy records’ signal content as well as the uncertainties involved. In this study, we empirically estimate signal-to-noise ratios (SNRs) of temperature proxy records used in global compilations of the mid to late Holocene. This is achieved through a comparison of proxy time series from close-by sites of three compilations and model time series data at the proxy sites from two transient Holocene climate model simulations. In all comparisons, we found the mean correlations of the proxy time series on centennial to millennial time scales to be rather low (R

Time series derived from paleoclimate archives are often irregularly sampled in time and thus not analysable using standard statistical methods such as correlation analyses. Although measures for the similarity between time series have been proposed for irregular time series, they do not account for the time scale dependency of the relationship. Stochastically distributed temporal sampling irregularities act qualitatively as a low-pass filter reducing the influence of fast variations from frequencies higher than about 0.5 (∆ $%&) () , where ∆ $%& is the maximum time interval between observations. This may lead to overestimated correlations if the true correlation increases with time scale. Typically, correlations are underestimated due to a non-simultaneous sampling of time series. Here, we investigated different techniques to estimate time scale dependent correlations of weakly irregularly sampled time series, with a particular focus on different resampling methods and filters of varying complexity. The methods were tested on ensembles of synthetic time series that mimic the characteristics of Holocene marine sediment temperature proxy records. We found that a linear interpolation of the irregular time series onto a regular grid, followed by a simple Gaussian filter was the best approach to deal with the irregularity and account for the time scale dependence. This approach had both, minimal filter artefacts, particularly on short time scales, and a minimal loss of information due to filter length.