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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. Using airborne imaging spectroscopy, we mapped estimated floristic composition at a landscape scale (a). Our results show a strong correlation between these airborne spectrally‐derived estimates and ground‐based floristic inventory data (b). 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 (c). [ABSTRACT FROM AUTHOR]

The consequences of biodiversity loss for ecosystem services largely depend on the functional identities of extirpated species. However, poor descriptions of spatial patterns of community functional composition across landscapes hamper accurate predictions, particularly in highly diverse tropical regions. Therefore, understanding how community functional composition varies across environmental gradients remains an important challenge., We sampled 15 functional traits in 800 Neotropical tree species across 13 forest plots representative of the broad climatic and soil gradients encompassed by three widespread lowland forest habitats (terra firme forests on clay-rich soils, seasonally flooded forests and white-sand forests) at opposite ends of Amazonia ( Peru and French Guiana). We combined univariate and multivariate approaches to test the magnitude and predictability of environmental filtering on community leaf and wood functional composition., Directional shifts in community functional composition correlated with environmental changes across the 13 plots, with denser leaves, stems and roots in forests occurring in environments with limited water and soil-nutrient availability. Critically, these relationships allowed us to accurately predict the functional composition of 61 additional forest plots from environmental data alone., Synthesis. Environmental filtering consistently shapes the functional composition of highly diverse tropical forests at large scales across the terra firme, seasonally flooded and white-sand forests of lowland Amazonia. Environmental factors drive and allow the prediction of variation in community functional composition among habitat types in Amazonian forests. [ABSTRACT FROM AUTHOR]