Summary Despite their critical importance for understanding the local effects of global climate change on biodiversity, glacial microrefugia are not well studied because they are difficult to detect by using classical palaeoecological or population genetics approaches. We used soil macrofossil charcoal analysis to uncover the presence of cryptic glacial refugia for European beech (Fagus sylvatica) and other tree species in the Landes de Gascogne (southwestern France). Using botanical identification and direct radiocarbon dating (140 14C‐dates) of macrofossil charcoal extracted from mineral soils, we reconstructed the glacial and postglacial history of all extant beech stands in the region (n = 11). Soil charcoal macrofossils were found in all sites, allowing the identification of up to at least 14 distinct fire events per site. There was direct evidence of the presence of beech during the last glacial period at three sites. Beech was detected during Heinrich stadial‐1, one of the coldest and driest intervals of the last glacial period in Western Europe. Together with previous results on the genetic structure of the species in the region, these findings suggest that beech persisted in situ in several microrefugia through full glacial and interglacial periods up to the present day.

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The forests of Amazonia are among the most biodiverse plant communities on Earth. Given the immediate threats posed by climate and land-use change, an improved understanding of how this extraordinary biodiversity is spatially organized is urgently required to develop effective conservation strategies. Most Amazonian tree species are extremely rare, but a small number are common across the region. Indeed, just 227 "hyperdominant" species account for more than 50% of all individuals > 10 cm dbh. Yet, the degree to which the phenomenon of hyperdominance is sensitive to tree size, the extent to which the composition of dominant species changes with size-class, and how evolutionary history constrains tree hyperdominance, all remain unknown. Here, we use a unique floristic dataset to show that,

The forests of Amazonia are among the most biodiverse on Earth, yet accurately quantifying how species composition varies through space (i.e., beta‐diversity) remains a significant challenge. Here, we use high‐fidelity airborne imaging spectroscopy from the Carnegie Airborne Observatory to quantify a key component of beta‐diversity, the distance decay in species similarity through space, across three landscapes in Northern Peru. We then compared our derived distance decay relationships to theoretical expectations obtained from a Poisson Cluster Process, known to match well with empirical distance decay relationships at local scales. We used an unsupervised machine learning approach to estimate spatial turnover in species composition from the imaging spectroscopy data. We first validated this approach across two landscapes using an independent dataset of forest composition in 49 forest census plots (0.1–1.5 ha). We then applied our approach to three landscapes, which together represented terra firme clay forest, seasonally flooded forest and white‐sand forest. We finally used our approach to quantify landscape‐scale distance decay relationships and compared these with theoretical distance decay relationships derived from a Poisson Cluster Process. We found a significant correlation of similarity metrics between spectral data and forest plot data, suggesting that beta‐diversity within and among forest types can be accurately estimated from airborne spectroscopic data using our unsupervised approach. We also found that estimated distance decay in species similarity varied among forest types, with seasonally flooded forests showing stronger distance decay than white‐sand and terra firme forests. Finally, we demonstrated that distance decay relationships derived from the theoretical Poisson Cluster Process compare poorly with our empirical relationships. Synthesis. Our results demonstrate the efficacy of using high‐fidelity imaging spectroscopy to estimate beta‐diversity and continuous distance decay in lowland tropical forests. Furthermore, our findings suggest that distance decay relationships vary substantially among forest types, which has important implications for conserving these valuable ecosystems. Finally, we demonstrate that a theoretical Poisson Cluster Process poorly predicts distance decay in species similarity as conspecific aggregation occurs across a range of nested scales within larger landscapes.