TY - JOUR
T1 - Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities
AU - Draper, Frederick C.
AU - Baraloto, Christopher
AU - Brodrick, Philip G.
AU - Phillips, Oliver L.
AU - Martinez, Rodolfo Vasquez
AU - Honorio Coronado, Euridice N.
AU - Baker, Timothy R.
AU - Zárate Gómez, Ricardo
AU - Amasifuen Guerra, Carlos A.
AU - Flores, Manuel
AU - Garcia Villacorta, Roosevelt
AU - V. A. Fine, Paul
AU - Freitas, Luis
AU - Monteagudo-Mendoza, Abel
AU - J. W Brienen, Roel
AU - Asner, Gregory P.
N1 - Funding Information:
Mary Anne Nyburg Baker; John D. and Catherine T. MacArthur Foundation; National Geographic Society, Grant/Award Number: 5472-9; Gordon and Betty Moore Foundation; Royal Society; Margaret A. Cargill Foundation; Grantham Foundation for the Protection of the Environment; W. M. Keck Foundation; Avatar Alliance Foundation; William R. Hearst III; Mary Anne Nyburg Baker and G. Leonard Baker, Jr; Seventh Framework Programme, Grant/Award Number: 283080; Natural Environment Research Council, Grant/Award Number: NE/B503384/1, NE/F005806/1 and NER/A/S/2000/0053
Funding Information:
This study was supported through a joint project between the Carnegie Institution for Science and the International Center for Tropical Botany at Florida International University. CAO data collection was supported by the John D. and Catherine T. MacArthur Foundation and the Avatar Alliance Foundation. We thank N Vaughn and D Knapp for processing the CAO data used in this analysis. Plot installation, fieldwork, and botanical identification by the authors and colleagues has been supported by several grants including a Gordon and Betty Moore Foundation grant to RAINFOR, the EU's Seventh Framework Programme (283080, ?GEOCARBON?) and NERC Grants to O.L.P. (Grants NER/A/S/2000/0053, NE/B503384/1, NE/F005806/1, and a NERC Postdoctoral Fellowship), and a National Geographic Society for supporting forest dynamics research in Amazonian Peru (grant #5472-95). O.L.P. is supported by an ERC Advanced Grant and is a Royal Society-Wolfson Research Merit Award holder. The Carnegie Airborne Observatory is made possible by grants and donations to GP Asner from the Avatar Alliance Foundation, Grantham Foundation for the Protection of the Environment, Gordon and Betty Moore Foundation, the John D. and Catherine T. MacArthur Foundation, W. M. Keck Foundation, the Margaret A. Cargill Foundation, Mary Anne Nyburg Baker and G. Leonard Baker, Jr., and William R. Hearst III.
Publisher Copyright:
© 2018 The Authors. Journal of Ecology © 2018 British Ecological Society
PY - 2019/3
Y1 - 2019/3
N2 - 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.
AB - 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.
KW - Amazonian forests
KW - beta diversity
KW - determinants of plant community diversity and structure
KW - distance decay
KW - imaging spectroscopy
KW - remote sensing
KW - tree biodiversity
KW - unsupervised clustering methods
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U2 - 10.1111/1365-2745.13067
DO - 10.1111/1365-2745.13067
M3 - Article
AN - SCOPUS:85054865289
SN - 0022-0477
VL - 107
SP - 696
EP - 710
JO - Journal of Ecology
JF - Journal of Ecology
IS - 2
ER -