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Summary1. Priority question exercises are becoming an increasingly common tool to frame future agendas in conservation and ecological science. They are an effective way to identify research foci that advance the field and that also have high policy and conservation relevance. 2. To date, there has been no coherent synthesis of key questions and priority research areas for palaeoecology, which combines biological, geochemical and molecular techniques in order to reconstruct past ecological and environmental systems on time-scales from decades to millions of years. 3. We adapted a well-established methodology to identify 50 priority research questions in palaeoecology. Using a set of criteria designed to identify realistic and achievable research goals, we selected questions from a pool submitted by the international palaeoecology research community and relevant policy practitioners. 4. The integration of online participation, both before and during the workshop, increased international engagement in question selection. 5. The questions selected are structured around six themes: human-environment interactions in the Anthropocene; biodiversity, conservation and novel ecosystems; biodiversity over long time-scales; ecosystem processes and biogeochemical cycling; comparing, combining and synthesizing information from multiple records; and new developments in palaeoecology. 6. Future opportunities in palaeoecology are related to improved incorporation of uncertainty into reconstructions, an enhanced understanding of ecological and evolutionary dynamics and processes and the continued application of long-term data for better-informed landscape management. 256-26750 priority research questions in palaeoecology 257 7. Synthesis. Palaeoecology is a vibrant and thriving discipline, and these 50 priority questions highlight its potential for addressing both pure (e.g. ecological and evolutionary, methodological) and applied (e.g. environmental and conservation) issues related to ecological science and global change.

Comparisons of climate model hindcasts with independent proxy data are essential for assessing model performance in non-analogue situations. However, standardized paleoclimate datasets for assessing the spatial pattern of past climatic change across continents are lacking for some of the most dynamic episodes of Earth's recent past. Here we present a new chironomid-based paleotemperature dataset designed to assess climate model hindcasts of regional summer temperature change in Europe during the late-glacial and early Holocene. Latitudinal and longitudinal patterns of inferred temperature change are in excellent agreement with simulations by the ECHAM-4 model, implying that atmospheric general circulation models like ECHAM-4 can successfully predict regionally diverging temperature trends in Europe, even when conditions differ significantly from present. However, ECHAM-4 infers larger amplitudes of change and higher temperatures during warm phases than our paleotemperature estimates, suggesting that this and similar models may overestimate past and potentially also future summer temperature changes in Europe.

Climate change challenges societal functioning, likely requiring considerable adaptation to cope with future altered weather patterns. Machine learning (ML) algorithms have advanced dramatically, triggering breakthroughs in other research sectors, and recently suggested as aiding climate analysis (Reichstein et al 2019 Nature 566 195-204, Schneider et al 2017 Geophys. Res. Lett. 44 12396-417).Although a considerable number of isolated Earth System features have been analysed with ML techniques, more generic application to understand better the full climate system has not occurred. For instance, ML may aid teleconnection identification, where complex feedbacks make characterisation difficult from direct equation analysis or visualisation of measurements and Earth System model (ESM) diagnostics. Artificial intelligence (AI) can then build on discovered climate connections to provide enhanced warnings of approaching weather features, including extreme events. While ESM development is of paramount importance, we suggest a parallel emphasis on utilising ML and AI to understand and capitalise far more on existing data and simulations.

Woody shrubs have increased in biomass and expanded into new areas throughout the Pan-Arctic tundra biome in recent decades, which has been linked to a biome-wide observed increase in productivity. Experimental, observational, and socio-ecological research suggests that air temperature-and to a lesser degree precipitation-trends have been the predominant drivers of this change. However, a progressive decoupling of these drivers from Arctic vegetation productivity has been reported, and since 2010, vegetation productivity has also been declining. We created a protocol to (a) identify the suite of controls that may be operating on shrub growth and expansion, and (b) characterise the evidence base for controls on Arctic shrub growth and expansion. We found evidence for a suite of 23 proximal controls that operate directly on shrub growth and expansion; the evidence base focused predominantly on just four controls (air temperature, soil moisture, herbivory, and snow dynamics). 65% of evidence was generated in the warmest tundra climes, while 24% was from only one of 28 floristic sectors. Temporal limitations beyond 10 years existed for most controls, while the use of space-for-time approaches was high, with 14% of the evidence derived via experimental approaches. The findings suggest the current evidence base is not sufficiently robust or comprehensive at present to answer key questions of Pan-Arctic shrub change. We suggest future directions that could strengthen the evidence, and lead to an understanding of the key mechanisms driving changes in Arctic shrub environments.