2021
DOI: 10.1016/j.funeco.2021.101054
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Relationships between macro-fungal dark diversity and habitat parameters using LiDAR

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Cited by 7 publications
(14 citation statements)
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“…This illustrates the strengths of lidar for characterizing habitats but also emphasizes that it cannot fully stand alone even when thoroughly using all the information in a lidar point cloud as we did here. This finding is supported by recent studies, e.g., by Valdez et al (2021) who studied the characteristics of high-dark-diversity fungal communities and by Zellweger et al (2016) who shed light on the relative importance of abiotic vs. structural factors for species richness at landscape scale in Swiss forests. It is notoriously difficult to estimate soil chemistry and -type using lidar (but see Li et al 2016).…”
Section: Discussionsupporting
confidence: 74%
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“…This illustrates the strengths of lidar for characterizing habitats but also emphasizes that it cannot fully stand alone even when thoroughly using all the information in a lidar point cloud as we did here. This finding is supported by recent studies, e.g., by Valdez et al (2021) who studied the characteristics of high-dark-diversity fungal communities and by Zellweger et al (2016) who shed light on the relative importance of abiotic vs. structural factors for species richness at landscape scale in Swiss forests. It is notoriously difficult to estimate soil chemistry and -type using lidar (but see Li et al 2016).…”
Section: Discussionsupporting
confidence: 74%
“…Today, the details from lidar data are so good that vegetation structure even in non-woody habitats with low vegetation can often be retrieved. For this reason, lidar is increasingly used in studies of local-scale ecology and biodiversity (de Vries et al 2021; Moeslund et al 2019; Mäyrä et al 2021), although it has never been used for studying plant dark diversity (but see Valdez et al, 2021).…”
Section: Introductionmentioning
confidence: 99%
“…The lidar variables represented both site mean values and standard deviation values to reflect variability within sites. The set of lidar variables encompassed: potential solar radiation (mean and std), adjusted solar radiation (i.e., solar radiation adjusted for vegetation cover; mean and std), amplitude (uncalibrated, but corrected for aircraft type and seasonality, see Valdez et al 2021), vegetation height (mean and std), vegetation cover (mean, std), mean vegetation density at 0-100 cm, 1 m-3 m, 3 m-10 m and 10 m-50 m, canopy openness (mean, std), terrain openness (mean, std), terrain slope (mean, std), echo ratio (i.e., canopy complexity; mean, std), heat load (std) and mean finescale (0.5m) terrain roughness (Appendix C).…”
Section: Methodsmentioning
confidence: 99%
“…For all lidar processing and calculation, we used the OPALS tools (Pfeifer et al 2014) version 2.3.1 in a Python 2.7 environment. For further details see Valdez et al (2021). The lidar variables represented both site mean values and standard deviation values to reflect variability within sites.…”
Section: Lidar-based Measuresmentioning
confidence: 99%
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