Analyzing remotely sensed structural and chemical canopy traits of a forest invaded by Prunus serotina over multiple spatial scales
Non-native invasive plant species can influence ecosystem functioning over broad spatial scales, but most research on ecosystem impacts has focused on the plot level covering sampling units of only a few square meters or less. We used a multi-scale approach to analyze structural and leaf chemical vegetation traits depending on the presence of non-native American black cherry (Prunus serotina) in a mixed deciduous forest at the plot level and at the forest stand level. Trait data were derived from remotely sensed maps of leaf area index (LAI), wood volume as well as canopy leaf nitrogen concentration (Nmass), phosphorous concentration (Pmass), and N:P ratio. Differences in these traits were compared between invaded and non-invaded areas at the plot level using 264 sampling units with a size of 25 m × 25 m and in 4119 forest management units (mean area: 7.6 ± 5.1 ha). Observed patterns between invaded and non-invaded areas were similar at both spatial scales. Invaded areas were characterized by less wood volume, indicating that lower standing biomass promotes the occurrence of P. serotina. In contrast, LAI did not differ between invaded and non-invaded areas. Furthermore, the presence of P. serotina trees had an impact on the chemical composition of the forest canopy by decreasing leaf N:P. While for Pmass, we found no differences in between invaded and non-invaded areas, for Nmass we observed an invasion effect, though only at the plot level. Using remotely sensed trait data proved valuable to evaluate the spatial relevance of invasion impacts over large areas.
KeywordsAlien plants Foliar stoichiometry Hyperspectral Imaging spectroscopy LiDAR
This study is part of the project DIARS (Detection of invasive plant species and assessment of their impact on ecosystem properties through remote sensing) funded by the ERA-Net BiodivERsA, with the national funders: ANR (Agence Nationale de la Recherche); BelSPO (Belgian Federal Science Policy Office); and DFG (Deutsche Forschungsgemeinschaft). Michael Ewald is funded through the DFG research grant SCHM 2153/9-1. Statistical models were calculated using the computational resource bwUniCluster funded by the Ministry of Science, Research and the Arts Baden-Württemberg and the universities of the state of Baden-Württemberg, Germany, within the framework program bwHPC. The authors would like to thank the Office National des Forêts for providing airborne LiDAR data. We also wish to thank Jérôme Piat, Luc Croisé, Fabien Spicher and Anthony Viaud for their help during field work as well as Javier Lopatin for proofreading.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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