Draper, FC, Baraloto, C, Brodrick, PG et al. (13 more authors) (2019) Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities. Journal of Ecology, 107 (2). pp. 696-710. ISSN 0022-0477
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.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2018 The Authors. Journal of Ecology © 2018 British Ecological Society. This is the peer reviewed version of the following article: Draper, FC, Baraloto, C, Brodrick, PG et al. (13 more authors) (2018) Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities. Journal of Ecology. ISSN 0022-0477, which has been published in final form at https://doi.org/10.1111/1365-2745.13067. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. |
Keywords: | Amazonian forests; beta diversity; determinants of plant community; diversity and structure; distance decay; imaging spectroscopy; remote sensing; tree biodiversity; unsupervised clustering methods |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Ecology & Global Change (Leeds) |
Funding Information: | Funder Grant number NERC NE/H011773/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Aug 2018 10:34 |
Last Modified: | 03 Sep 2019 08:23 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1111/1365-2745.13067 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:134558 |