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Abstract. Deep-time palynological studies are necessary to evaluate plant and fungal distribution under warmer-than-present scenarios such as those of the Middle Miocene. Previous palynological studies from southern McMurdo Sound, Antarctica (SMS), have provided unique documentation for Neogene environments in the Ross Sea region during a time of pronounced global warming. The present study builds on these studies and provides a new climate reconstruction using the previously published SMS pollen and plant spore data. Additionally, 44 SMS samples were reanalyzed with a focus on the fungal fraction of the section to evaluate the fungal distribution under warmer than present conditions. The probability-based climate reconstruction technique (CREST) was applied to provide a new plant-based representation of regional paleoclimate for this Miocene Climatic Optimum (MCO) locality. CREST reconstructs a paleoclimate that is warmer and significantly wetter than present in SMS during the MCO, with mean annual precipitation reconstructed at 1147 mm yr−1 (95 % confidence range: 238–2611 mm yr−1) and a maximum mean annual temperature of 10.3 ∘C (95 % confidence range: 2.0–20.2 ∘C) for the warmest intervals of the MCO. The CREST reconstruction fits within the Cfb Köppen–Geiger climate class during the MCO of SMS. This new reconstruction agrees with previous reconstructions using various geochemical proxies. The fungal palynological analyses yielded surprising results, with only a single morphotype recovered, in low abundance, with concentrations ranging up to 199 fungi per gram of dried sediment. The taxa present belongs to the Apiosporaceae family and are known to be adapted to a wide range of climate and environmental conditions. As fungi are depauperate members of the SMS MCO palynofloras and because the one morphotype recovered is cosmopolitan, using the fungi record to confirm a narrow Köppen–Geiger climate class is impossible. Overall, the study demonstrates refinement of plant-based paleoclimatic reconstructions and sheds light on the limited presence of fungi during the MCO in Antarctica.

 

Fossil fungi from periods warmer than modern climates provide unique insights into the future impacts of anthropogenic climate change. Here we report the fossil fungal assemblage from the late Middle Miocene Kenslow Member of central England, associated with climatic conditions warmer than the present-day. The identification of 110 morphotypes, which primarily relate to moist environments and the presence of wood, have been used to develop a new nearest living relative palaeoclimate reconstruction. The fungal assemblage indicates a Köppen–Geiger climate class, represented by temperate conditions, no dry season, and warm summers. This new fungal-based palaeoclimate reconstruction technique holds exciting potential to explore critically important but poorly understood palaeoenvironments, and the resulting qualitative inferences align well with previously published palaeobotanical quantitative estimates of palaeoclimate. These findings show that diverse fungal assemblages can successfully be used to reconstruct past climates for the first time. 

The middle Miocene Climate Optimum (MMCO) was the warmest interval of the last 23 million years and is one of the best analogs for proposed future climate change scenarios. Fungi play a key role in the terrestrial carbon cycle as dominant decomposers of plant debris, and through their interactions with plants and other organisms as symbionts, parasites, and endobionts. Thus, their study in the fossil record, especially during the MMCO, is essential to better understand biodiversity changes and terrestrial carbon cycle dynamics in past analogous environments, as well as to model future ecological and climatic scenarios. The fossil record also offers a unique long-term, large-scale dataset to evaluate fungal assemblage dynamics across long temporal and spatial scales, providing a better understanding of how ecological factors influenced assemblage development through time. In this study, we assessed the fungal diversity and community composition recorded in two geological sections from the middle Miocene from the coal mines of Thailand and Slovakia. We used presence-absence data to quantify the fungal diversity of each locality. Spores and other fungal remains were identified to modern taxa whenever possible; laboratory codes and fossil names were used when this correlation was not possible. This study represents the first of its kind for Thailand, and it expands existing work from Slovakia. Our results indicate a total of 281 morphotaxa. This work will allow us to use modern ecological data to make inferences about ecosystem characteristics and community dynamics for the studied regions. It opens new horizons for the study of past fungal diversity based on modern fungal ecological analyses. It also sheds light on how global variations in fungal species richness and community composition were affected by different climatic conditions and under rapid increases of temperature in the past to make inferences for the near climatic future. 

Taxonomic resolution is a major challenge in palynology, largely limiting the ecological and evolutionary interpretations possible with deep-time fossil pollen data. We present an approach for fossil pollen analysis that uses optical superresolution microscopy and machine learning to create a quantitative and higher throughput workflow for producing palynological identifications and hypotheses of biological affinity. We developed three convolutional neural network (CNN) classification models: maximum projection (MPM), multislice (MSM), and fused (FM). We trained the models on the pollen of 16 genera of the legume tribe Amherstieae, and then used these models to constrain the biological classifications of 48 fossilStriatopollisspecimens from the Paleocene, Eocene, and Miocene of western Africa and northern South America. All models achieved average accuracies of 83 to 90% in the classification of the extant genera, and the majority of fossil identifications (86%) showed consensus among at least two of the three models. Our fossil identifications support the paleobiogeographic hypothesis that Amherstieae originated in Paleocene Africa and dispersed to South America during the Paleocene-Eocene Thermal Maximum (56 Ma). They also raise the possibility that at least three Amherstieae genera (Crudia,Berlinia, andAnthonotha) may have diverged earlier in the Cenozoic than predicted by molecular phylogenies.