Remote sensing is commonly used to map the presence of invasive alien plant species across large areas. However, remote sensing also has potential to predict fractional cover, which is more closely linked to the potential ecosystem impact of invasive alien plant species. Here, we mapped the fractional cover of the invasive bryophyte Campylopus introflexus using high resolution imaging spectroscopy (233 bands, 490–2430 nm, 3 m × 3 m pixel size) in different habitat types within a coastal dune ecosystem. Maps were predicted from generalized partial least squares (gPLS) regression models trained with cover information from 266 field plots (3 m × 3 m). Models were calibrated using different data subsets, following a spatially blocked subsampling design. In addition, the relationship between C. introflexus fractional cover and plant species richness was evaluated using a subset of plot data. Fractional cover estimates from gPLS models resulted in R2 values of 0.64 ± 0.17 and an RMSE of 0.14 ± 0.02 based on independent validation data. Field observations showed a negative relationship between C. introflexus fractional cover and local plant species richness for pixels with a fractional cover above 44%. This threshold was exceeded in 4.3–7.1% of the studied area depending on the habitat type. Our study demonstrates that remote sensing can be used to map the fractional cover of an invasive bryophyte in a coastal dune ecosystem. Combining cover maps with a threshold value derived from an abundance-impact relationship we were able to highlight areas with potential negative impact on local plant species richness. This method can be implemented on other species and other ecosystem properties affected by plant invasions, al- lowing to highlight potential invasion impacts in a spatially explicit manner.

Assessing the impact of an invasive bryophyte on plant species richness using high resolution imaging spectroscopy

Rocchini, Duccio;
2020

Abstract

Remote sensing is commonly used to map the presence of invasive alien plant species across large areas. However, remote sensing also has potential to predict fractional cover, which is more closely linked to the potential ecosystem impact of invasive alien plant species. Here, we mapped the fractional cover of the invasive bryophyte Campylopus introflexus using high resolution imaging spectroscopy (233 bands, 490–2430 nm, 3 m × 3 m pixel size) in different habitat types within a coastal dune ecosystem. Maps were predicted from generalized partial least squares (gPLS) regression models trained with cover information from 266 field plots (3 m × 3 m). Models were calibrated using different data subsets, following a spatially blocked subsampling design. In addition, the relationship between C. introflexus fractional cover and plant species richness was evaluated using a subset of plot data. Fractional cover estimates from gPLS models resulted in R2 values of 0.64 ± 0.17 and an RMSE of 0.14 ± 0.02 based on independent validation data. Field observations showed a negative relationship between C. introflexus fractional cover and local plant species richness for pixels with a fractional cover above 44%. This threshold was exceeded in 4.3–7.1% of the studied area depending on the habitat type. Our study demonstrates that remote sensing can be used to map the fractional cover of an invasive bryophyte in a coastal dune ecosystem. Combining cover maps with a threshold value derived from an abundance-impact relationship we were able to highlight areas with potential negative impact on local plant species richness. This method can be implemented on other species and other ecosystem properties affected by plant invasions, al- lowing to highlight potential invasion impacts in a spatially explicit manner.
2020
Ewald, Michael; Skowronek, Sandra; Aerts, Raf; Lenoir, Jonathan; Feilhauer, Hannes; Van De Kerchove, Ruben; Honnay, Olivier; Somers, Ben; Garzón-López, Carol X.; Rocchini, Duccio; Schmidtlein, Sebastian
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/716944
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