Measuring beta-diversity by remote sensing: a challenge for biodiversity monitoring

Rocchini, Duccio; Luque, Sandra; Pettorelli, Nathalie; Bastin, Lucy; Doktor, Daniel; Faedi, Nicolo; Feilhauer, Hannes; Feret, Jean-Baptiste; Foody, Giles M.; Gavish, Yoni; Godinho, Sergio; Kunin, William E.; Lausch, Angela; Leitao, Pedro J.; Marcantonio, Matteo; Neteler, Markus; Ricotta, Carlo; Schmidtlein, Sebastian; Vihervaara, Petteri; Wegmann, Martin; Nagendra, Harini

Abstract

Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, overall when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this view, airborne or satellite remote sensing allow to gather information over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (beta-diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript we propose novel techniques to measure beta-diversity from airborne or satellite remote sensing, mainly based on: i) multivariate statistical analysis, ii) the spectral species concept, iii) self-organizing feature maps, iv) multi- dimensional distance matrices, and the v) Rao's Q diversity. Each of these measures allow to solve one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating beta-diversity from remotely sensed imagery and potentially relate them to species diversity in the field.

Citation

Rocchini, D., Luque, S., Pettorelli, N., Bastin, L., Doktor, D., Faedi, N., Feilhauer, H., Feret, J.-B., Foody, G. M., Gavish, Y., Godinho, S., Kunin, W. E., Lausch, A., Leitao, P. J., Marcantonio, M., Neteler, M., Ricotta, C., Schmidtlein, S., Vihervaara, P., Wegmann, M., & Nagendra, H. (2018). Measuring beta-diversity by remote sensing: a challenge for biodiversity monitoring. Methods in Ecology and Evolution, 9(8), 1787-1798. https://doi.org/10.1111/2041-210X.12941

• Journal Article Type: Article
• Acceptance Date: Nov 11, 2017
• Online Publication Date: Aug 6, 2018
• Publication Date: Aug 6, 2018
• Deposit Date: Jan 17, 2018
• Publicly Available Date: Aug 7, 2019
• Journal: Methods in Ecology and Evolution
• Electronic ISSN: 2041-210X
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 8
• Pages: 1787-1798
• DOI: https://doi.org/10.1111/2041-210X.12941
• Keywords: Beta-diversity, Kohonen self-organising feature maps, Rao's Q diversity index, remote sensing, satellite imagery, Sparse Generalized Dissimilarity Model, spectral species concept
• Public URL: https://nottingham-repository.worktribe.com/output/894413
• Publisher URL: https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12941
• Contract Date: Jan 17, 2018