We compare a Bayesian modelling-based technique with weighted averaging (WA) and weighted averaging-partial least squares (WA-PLS) regression in pollen-based summer temperature transfer function calibration. We test the methods using a new, 113-sample calibration set from Estonia, Lithuania and European Russia, and a Holocene fossil pollen sequence from Lake Kharinei, a previously studied lake in northeast European Russia. We find WA-PLS to outperform WA, probably because of smaller edge-effect biases in the ends of the calibration set gradient. The Bayesian-based calibration models show further improved performance compared with WA-PLS in leave-one-out cross-validation, while additional h-block cross-validation shows the Bayesian method to be little affected by spatial autocorrelation. Comparison with independent climate proxies reveals, however, some clear biases in the Bayesian palaeotemperature reconstructions, likely reflecting in part some specific limitations of our calibration set. As the selected prior parameters can significantly affect both Bayesian cross-validation performance and reconstructions, there is a clear need to further test the Bayesian method in different geographic contexts and over different timescales, with special attention given to the selection of the most realistic priors in each situation. In general, our finding that statistically well-performing transfer functions may produce clearly differing palaeotemperature reconstructions urges caution in transfer functionbased inferences. We additionally test a spatially restricted, 58-sample subset of the full 113-sample calibration set. We find some reduced biases with the smaller set, likely because of complex, partially bimodal responses of several taxa along the longer temperature gradient, ill-suited for calibration methods assuming unimodal responses to climate.

Holocene (the last 12,000 years) temperature variation, including the transition out of the last Ice Age to a warmer climate, is reconstructed at multiple locations in southern Finland, Sweden and Estonia based on pollen fossil data from lake sediment cores. A novel Bayesian statistical approach is proposed that allows the reconstructed temperature histories to interact through shared environmental response parameters and spatial dependence. The prior distribution for past temperatures is partially based on numerical climate simulation. The features in the reconstructions are consistent with the quantitative climate reconstructions based on more commonly used reconstruction techniques. The results suggest that the novel spatiotemporal approach can provide quantitative reconstructions that are smoother, less uncertain and generally more realistic than the sitespecific individual reconstructions.

Fire is a major disturbance agent in the boreal forest, influencing many current and future ecosystem conditions and services. Surprisingly few studies have attempted to improve the accuracy of fire-event reconstructions even though the estimates of the occurrence of past fires may be biased, influencing the reliability of the models employing those data (e.g. C stock, cycle). This study aimed to demonstrate how three types of fire proxies – fire scars from tree rings, sedimentary charcoal and, for the first time in this context, fungal spores of Neurospora – can be integrated to achieve a better understanding of past fire dynamics. By studying charcoal and Neurospora from sediment cores from forest hollows, and the fire scars from tree rings in their surroundings in the southern Fennoscandian and western Russian boreal forest, we produced composite fire-event data sets and fire-event frequencies, and estimated fire return intervals. Our estimates show that the fire return interval varied between 126 and 237 years during the last 11,000 years. The highest fire frequency during the 18th–19th century can be associated with the anthropogenic influence. Importantly, statistical tests revealed a positive relationship between other fire event indicators and Neurospora occurrence allowing us to pinpoint past fire events at times when the sedimentary charcoal was absent, but Neurospora were abundant. We demonstrated how fire proxies with different temporal resolution can be linked, providing potential improvements in the reliability of fire history reconstructions from multiple proxies.