This data package contains data from: Floristic plot data, spectrally derived data and associated analytical code (R and python) to replicate analysis in: 'Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities.'
This dataset is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-SA 4.).
When using this data, please cite the original article:
Frederick C. Draper, Christopher Baraloto, Philip G. Brodrick, Oliver L. Phillips, Rodolfo Vasquez Martinez, Euridice N. Honorio Coronado, Timothy R. Baker, Ricardo Zárate Gómez, Carlos A. Amasifuen Guerra, Manuel Flores, Roosevelt Garcia Villacorta, Paul V. A. Fine, Luis Freitas, Abel Monteagudo-Mendoza, Roel J.W Brienen and Gregory P. Asner. Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities. Journal of Ecology, 2018.
Additionally, please cite the data package:
Frederick C. Draper, Christopher Baraloto, Philip G. Brodrick, Oliver L. Phillips, Rodolfo Vasquez Martinez, Euridice N. Honorio Coronado, Timothy R. Baker, Ricardo Zárate Gómez, Carlos A. Amasifuen Guerra, Manuel Flores, Roosevelt Garcia Villacorta, Paul V. A. Fine, Luis Freitas, Abel Monteagudo-Mendoza, Roel J.W Brienen and Gregory P. Asner. Floristic plot data, spectrally derived data and associated analytical code (R and python) to replicate analysis in: 'Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities'. ForestPlots.net 2018.
ForestPlots.NET DOI: 10.5521/forestplots.net/2018_4
Abstract
Data package description: Floristic plot data, spectrally derived data and associated analytical code (R and python) to replicate analysis in: 'Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities' Draper et al 2018, Journal of Ecology.
Paper abstract:
1. 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.
2. 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.
3. 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.
4. 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.
