GENERAL INFORMATION



1. Title of Dataset: Biodiversity facets, canopy structure and surface temperature of grassland communities



2. Author Information

	A. Principal Investigator Contact Information

		Name: Claudia Guimaraes-Steinicke	

		Institution: University of Leipzig

		Address: Johannisallee 21-23, 04103 Leipzig, Germany 

		Email: claudia.steinicke@uni-leipzig.de



	B. Associate or Co-investigator Contact Information

		Name: Christian Wirth

		Institution: University of Leipzig

		Address: Johannisallee 21-23, 04103 Leipzig, Germany 

		Email: cwirth@uni-leipzig.de





3. Date of data collection (single date, range, approximate date): 2014-05-31 and 2014-08-20 



4. Geographic location of data collection: 50°55´ N, 11°35`E, 130 m above sea level, Jena, Thuringia, Germany 


DATA & FILE OVERVIEW



1. File List: 


1.1 File_name: SEM_Structure_metrics_temperature_May.csv

Brief description: This dataset comprehend data collected in May. The table has 17 columns and 92 rows which correspond to the Trait-Based Experiment design.
Moreover, it contains data as plot, block and sown species, 8 6 canopy structure metrics, 2 columns of NDVI blue and red and 3 columns referring to mean, coefficient of variation and difference between air temperature and leaf surface temperature.  

1.2 File_name: SEM_Structure_metrics_temperature_August.csv

Brief description: This dataset comprehend data collected in August and it follows the same structure and metrics as in May. 

 
METHODOLOGICAL INFORMATION


1. Instrument- or software-specific information needed to interpret the data: 

1.1 To perform a non-destructive measurement of plant community canopy structure we used a terrestrial laser scanner (TLS) Faro Focus 3D X330 (FARO Technologies Inc., 2011)

1.2 Leaf area index was measure using the LAI-2000 plant canopy analyzer (LI-Cor, Inc, 2013)

1.3 To calculate voxel grids of the 3D point clouds we used the function ‘vox’ from the R package VoxR (Lecigne et al., 2014).

1.4 To calculate canopy variation we used the Poisson surface reconstruction was generated using the open-source software CloudCompare (CloudCompare Omnia, 2019).

1.5 To evaluate the degree of clumpiness within the plant communities we rasterized the 3D points 
and computed Geary´s index, an identifier of cluster points with similar attributes, assessed by the pixel spatial autocorrelation. We used the function Geary from the R package “raster”.  

1.7 to obtain mean and variation of surface temperature, The IRSoft software processed both thermal images and RGB converting the radiation into the surface temperature among image pixels. 

1.6 To analyse the structure equation models, we used R (version 3.6.3) package “PiecewiseSEM” version 2.1.0  (Lefcheck, 2016).



DATA-SPECIFIC INFORMATION FOR:SEM_Structure_metrics_temperature_May.csv and SEM_Structure_metrics_temperature_August.csv


1. Number of variables: 17

2. Number of cases/rows: 92

3. Variable List: 

Plot: Factor code and number of plot without space

Block: Factor code

MeanH : Mean height of plant communities extracted by the TLS

LAI: leaf area index

Evenness: the mean proportion of filled voxels across strata of vegetation height, calculated as the sum of all five voxel strata volumes divided by 5

Center of Gravity:it used the volume of voxel grids per height strata to identify the location with the highest density of points. 
This location was measured in terms of the height-weighted average volume allocation of the community. 
We then calculated the center of gravity by multiplying each stratum's volume with the mean height of the strata and dividing by the total community volume. Center of gravity range from 1 to 5, in which 1 is the bottom layer (0-20 cm) and five the top canopy (80-100 cm).

Can_var: canopy surface variation was calculated using the surface reconstruction method, which fits a mesh on the 3D point cloud density of each plot (the filtered point clouds and not voxel grids) (Attene & Spagnuolo, 2000). 
We applied the Poisson Surface Reconstruction method, which fits a mesh on all oriented points (perpendicular vectors to the tangential plane to the surface at that point)

PCA1: scores of PCA axis 1 because it explained 49% of the variance. The PCA was based on the community-weighted means of species functional traits (CWM), which describe the mean trait values of all species weighted by their abundances in a community (Lavorel et al., 2008)

FDis: functional dispersion index (FDis), representing the sum of abundance-weighted distances from the center of all species in the multidimensional trait space (Laliberté & Legendre, 2010)

Clumpiness: it evaluated the size and distribution of clusters in the spatial arrangement of the point cloud into two dimensions based on the rasterized 3D point clouds.

FlowerR: we calculated the Normalized Green-Red Difference Index from the RBG pictures taken from the thermal camera (NGRDI, the difference between the green and red bands divided by their sum (Pérez et al., 2000)

FlowerB: Normalized Green-Blue Difference Index (NGBDI, the difference between the green and the blue bands divided by their sum (Wang Xiaoqin et al., 2015) 

Tmean:  mean temperature per plot calculated from the thermal matrix (registered pixel temperature).

TCV: Coefficient of variation of temperature (also obtained from the thermal matrix

Tair: Air temperature (obtained from the Weather Station in the field of Jena Experiment)

DeltaT: Difference between air temperature and leaf surface temperature

