DOI: 10.5061/dryad.866t1g1q1
title: Biodiversity facets, canopy structure and surface temperature of grassland communities

description: Canopy structure is an important driver of the energy budget of the
 grassland ecosystem and is, at the same time, altered by plant diversity.
 Diverse plant communities typically have taller and more densely packed
 canopies than less diverse communities. With this, they absorb more
 radiation, have a higher transpiring leaf surface, and are better coupled
 to the atmosphere which leads to cooler canopy surfaces. However, whether
 plant diversity generally translates into a cooling potential remains
 unclear and lacks empirical evidence. Here, we assessed how functional
 identity, functional diversity, and species richness of grassland
 communities in the Jena Experiment predict the mean and variation of plant
 surface temperature mediated via effects of canopy structure. Using
 terrestrial laser scanning, we estimated canopy structure describing
 metrics of vertical structure (mean height, LAI), the distribution
 (evenness), and the highest allocation (center of gravity) of biomass
 along height strata. As metrics of horizontal structure, we considered
 community stands gaps, canopy surface variation, and emergent flowers. We
 measured surface temperature with a thermal camera. We used SEM models to
 predict biodiversity effects on the surface temperature during two
 seasonal peaks of biomass. Before the first cut in May, herb-dominated
 communities directly promoted lower leaf surface temperatures. However,
 communities with a lower center of gravity (mostly herb-dominated) also
 increased canopy surface temperatures compared with grass-dominated
 communities with higher biomass stored in the top canopy. Grass-dominated
 communities showed a smaller variation of surface temperatures, which was
 also positively affected by species richness via an increase in mean
 height. In August, mean surface temperature decreased with increasing
 community clumpiness and LAI. The variation of surface temperature was
 greater in herb-dominated than in grass-dominated communities and
 increased with plant species richness (direct effects). Synthesis: The
 mean and variation of canopy surface temperature were driven by
 differences in functional group composition (herbs- vs. grass dominance)
 and to a lesser extent by plant diversity. These effects were partly
 mediated by the metrics of canopy structure but also by direct effects
 unrelated to the structural metrics considered.  
description: Our field data was collected within the
 Trait-Based Biodiversity Experiment in 2014 (TBE; Ebeling et al., 2014) at
 the Jena Experiment site (Thuringia, Germany; 50°55´ N, 11°35`E, 130 m
 above sea level (Roscher et al., 2004; Weisser et al., 2017). We conducted
 this study in 92 plots of the Trait-Based experiment (3.5 m x 3.5 m size),
 comprehending the two species pools, with a gradient of plant species
 richness of 1 to 8 species. We performed a non-destructive measurement of
 plant community canopy structure at high resolution, we used a terrestrial
 laser scanner (TLS) Faro Focus 3D X330 (FARO Technologies Inc., 2011). We
 scanned 92 plots on April 31st (the first peak of biomass) and August
 20th, 2014 (the second biomass peak). The TLS was mounted upside-down on a
 tripod that was elevated 3.35 m above ground level. The legs of the tripod
 were centered on permanent survey markers to guarantee identical scanning
 areas on both dates. We extracted an area of 3.75 m² (1.5 m x 2.5 m) in
 each plot below the scanner to reduce the effect of shadows within scans.
 The point clouds of the 92 plots were filtered using statistical outlier
 removal (SOR) and noise filter. We used the 3D point clouds from
 terrestrial laser scanning to calculate metrics characterizing vertical
 and horizontal dimensions of the community canopy structure. We produced
 height-based metrics from the point cloud of each community. We used mean
 height as the first vertical dimension metric. To characterize vertical
 space-filling properties, we calculated the evenness and the center of
 gravity of the point cloud. Evenness reports the homogeneity of the point
 cloud density in their vertical distribution, while the center of gravity
 identifies the height stratum (definition see below) with the highest
 density of points (Spehn et al., 2000; Barry et al., 2020). As a baseline
 for only these two vertical metrics (evenness and center of gravity), we
 calculated voxel grids from the 3D point cloud for each plot. For each
 scan, a voxel grid of 5 cm was created containing at least one laser
 return, and the volume was then calculated as the product of the cell area
 and the attributed height. We used the function ‘vox’ from the R package
 VoxR (Lecigne et al., 2014). We used the voxel grids to define five
 different strata of height (0.3 - 20 cm, 20 - 40 cm, 40 - 60 cm, 60 – 80
 cm, and 80 - 100 cm). For every stratum, we applied the method ‘Sum of
 Voxel,’ which calculated the sum of all voxels separately for each of the
 five strata. As a result, we obtained volumetric data based on 3D point
 clouds for five different strata and the community canopy height. Based on
 this information, the evenness metric represents the mean proportion of
 filled voxels across strata of vegetation height, calculated as the sum of
 all five voxel strata volumes divided by 5. The center of gravity, in
 turn, 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).
 Further, the leaf area index was also measured at the same time in all 92
 plots using the LAI-2000 plant canopy analyzer (LI-Cor, Inc, 2013). Ten
 random measurements were averaged to a mean of LAI value per plot. Hence,
 we used LAI as an additional vertical dimensional metric to characterize
 plant ground area covered by the plant community. To assess the horizontal
 heterogeneity of the plant community for each plot, we also calculated two
 horizontal metrics describing the canopy surface variation and clumpiness.
 We used 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) (Kazhdan & Hoppe, 2019). After producing the surface mesh
 for all plots, a surface area of the mesh in square meters was calculated
 and divided by the area of the plot (3.75 m²). The variation metric is a
 dimensionless ratio between the mesh surface area and the ground area. For
 clumpiness, we 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. For this, we 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”. The two response variables, the mean and the coefficient
 of variation (hereafter CV) of community surface temperature were obtained
 using the Testo 882 Thermal Imaging Camera, which also recorded RGB images
 of all 92 plots (for example, Figure 3). We obtained the thermal data and
 terrestrial laser scans within two days. All thermal measurements were
 carried out around noon (12:30 – 13:30), at 150 cm height, and facing
 north. The thermal camera settings controlled the canopy's emissivity
 as 0.95 with reflectance temperature at 20°C. The sensor detects long-wave
 infrared radiation in the spectral range from 7.5 to 14 μm and has a
 thermal sensitivity of 50 mK at +30°C and accuracy of ±2.0°C. With the
 thermal matrix (registered pixel temperature with an original resolution
 of 640 x 480), we computed the mean and the coefficient of variation of
 surface temperature for each plot. As flower heads are often 10 K warmer
 than the surrounding leaves of herbs and grasses, we calculated the
 Normalized Green-Red Difference Index (NGRDI, the difference between the
 green and red bands divided by their sum (Pérez et al., 2000). Further, we
 also calculated the Normalized Green-Blue Difference Index (NGBDI, the
 difference between the green and the blue bands divided by their sum (Wang
 Xiaoqin et al., 2015) better to distinguish the hot spots of
 inflorescences and green vegetation. We expected that biodiversity effects
 on mean canopy surface temperature are indirectly mediated by predictors
 related to vertical structure metrics, while biodiversity effects on
 temperature CV are mediated by the horizontal structure. To test these
 assumptions further, we constructed a more detailed formal hypothesis
 using linear mixed-effects models within a PiecewiseSEM (Lefcheck, 2016).
 we ran the initial SEM model as a list of causal relationships between
 canopy structure and biodiversity facets. The last linear model inside the
 SEM was between the mean and CV of surface temperature and biodiversity
 facets (functional identity, dispersion, and species richness) and all
 canopy structure metrics to test the fit of the model to the data. Second,
 we inspected this initial SEM model results for goodness-of-fit tests for
 both the full and causal relationships, we then added the predictors that
 significantly improved the model fit with P values higher than
 0.05.
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