In:
Methods in Ecology and Evolution, Wiley, Vol. 12, No. 11 ( 2021-11), p. 2174-2183
Kurzfassung:
To understand how ecological communities will respond to global change we need new tools and datasets on species across large spatial and temporal scales. Hyperspectral reflectance ‘spectra’ capture a promising set of traits that show potential to be scaled up in time and space via remote sensing. Thus far, spectra have been shown to distinguish the taxa and trait responses of a substantial number of species within a plethora of vascular plant communities, but not yet for biological soil crust communities (biocrusts). Here, we assess if spectra can be applied to identify biocrust species and their trait variation. We collected biocrust specimens across an aridity gradient spanning 650 km within drylands of Eastern Australia and acquired their spectra, over 12,700 spectral readings, with a high‐resolution radiospectrometer. A machine learning method (random forests) was used to assess how well the spectra of biocrust specimens could distinguish their species and broader structural and chemical traits. Spectra were able to differentiate a substantial number of biocrust species (35) with considerable accuracy (~78.5%). Furthermore, spectral features related to chemical traits were found to primarily drive species spectral differences. Synthesis . Our findings establish that biocrust species hold unique and detectable spectral responses, providing an important basis for remote sensing applications on biocrust species and their trait responses across dryland systems.
Materialart:
Online-Ressource
ISSN:
2041-210X
,
2041-210X
DOI:
10.1111/2041-210X.13690
Sprache:
Englisch
Verlag:
Wiley
Publikationsdatum:
2021
ZDB Id:
2528492-7
