Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities. (12th October 2018)

• Record Type: Journal Article
• Authors: Draper, Frederick C.; Baraloto, Christopher; Brodrick, Philip G.; Phillips, Oliver L.; Martinez, Rodolfo Vasquez; Honorio Coronado, Euridice N.; Baker, Timothy R.; Zárate Gómez, Ricardo; Amasifuen Guerra, Carlos A.; Flores, Manuel; Garcia Villacorta, Roosevelt; V. A. Fine, Paul; Freitas, Luis; Monteagudo‐Mendoza, Abel; J. W Brienen, Roel; Asner, Gregory P.
• Editors: Edwards, David
• Is Part Of: Journal of ecology. Volume 107:Number 2(2019:Mar.)
• Page Start: 696
• Page End: 710
• Publication Date: 2018-10-12
• Subjects: Amazonian forests -- beta diversity -- determinants of plant community diversity and structure -- distance decay -- imaging spectroscopy -- remote sensing -- tree biodiversity -- unsupervised clustering methods; Plant ecology -- Periodicals; 577.05
• DOI: 10.1111/1365-2745.13067
• Languages: English
• ISSNs: 0022-0477

Abstract

Abstract: 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. Abstract : 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).