Tree diversity drives multiple facets of bee diversity via microenvironment

Wild bees are widely distributed and effective pollinators, yet they face
significant threats such as degradation of forests. Forest restoration has
been advocated as a strategy to mitigate these threats and stabilize
biodiversity. However, there is a lack of understanding of the ecological
consequences of forest restoration on bee diversity, particularly
regarding interactions with tree diversity and microenvironment. Using
data from the world’s largest tree diversity experiment (BEF-China), this
study examines how tree species richness, canopy cover, understory
vegetation, and microclimatic conditions affect bee diversity in the
context of forest restoration. Our analysis of bee diversity data (8,341
individuals from 79 species) revealed that these biotic factors had
distinct effects on three dimensions of bee diversity. Specifically,
canopy cover had a negative effect on bee taxonomic diversity but a
positive effect on phylogenetic and functional diversity. However, these
patterns were reversed when cover of understory vegetation was accounted
for. Moreover, tree species richness exerted an indirect influence on bee
diversity through understory microenvironment. Our findings provide nuance
into how tree species richness shapes bee communities via vegetation cover
and microclimate, which is informative on habitat characteristics in
forest restoration and conservation that better enable the safeguarding of
pollinators.

Study sites The study was
carried out in the BEF-China Biodiversity Experiment which is located in
forests of Jiangxi province, a subtropical region (29°08′–29°11′N,
117°90′–117°93′E). For this study, 66 plots were selected randomly for the
two sites (33 plots for each site), which covered a tree species richness
gradient from 0 to 24 (for each site, 1 plot without artificial planted
trees, 16 plots with monocultures, 8 plots with 2 species, 4 plots with 4
species, 2 plots with 8 species, 1 plot with 16 species and 1 plot with 24
species). Environmental
factors Four quadrats were set on the
diagonal (next to the traps) and one was set in the centre of each plot to
measure the coverage of understory vegetation, of which the size was 1 m*1
m (see Fig S1). Above each quadrat, canopy cover was recorded through
hemispherical pictures taken at 1 m above ground with a 180-mm fish eye
lens and then calculated as percentage of black area of total image size
using Gap Light Analyzer 2.0 (Frazer et al., 1999). Then, the mean of the
canopy cover and understory coverage were calculated for each plot. For
microclimate, temperature and humidity data were recorded in 15-minute
intervals with sensors mounted 1 m above ground (Shandong Renke Control
Technology Co., Ltd., China). The monthly temperature and humidity during
the sampling events were calculated to represent understory
microclimate. Bee sampling and molecular
work Blue vane traps and three-colored pan
traps were both used to collect bees in June and September of 2022, and
April, June and September of 2023. Two vane traps were put at a distance
of 1.5 m from ground, while two groups of pan traps (yellow, white and
blue) were placed at a distance of 0.5 m on the second diagonal of each
plot. Each trap was partially filled with water, with few drops of
detergent to break the water surface tension. For each collection event,
samples in the traps were collected after about 24 hours, conducted three
times (totally 72 h). COI DNA barcode region (~650 bp) was amplified using
universal primer pairs (LCO1490: GGTCA ACAAA TCATA AAGAT ATTGG and HCOout:
CCAGG TAAAA TTAAA ATATA AACTT C). Molecular delimitation and taxonomic
assignment followed the pipeline of Xie et at. (2023) (Xie et al.,
2023). Bee functional
traits Functional traits of our captured
bees were determined either by new measurement (e.g. morphological traits)
or phylogeny-based prediction using species-level reference data of
phylogeny terminals and life history records. We measured morphometric
traits of bee individuals using a Zeiss Discovery V20 stereomicroscope. If
the length exceeded the microscope range, we used a digital calliper. Five
such traits were used, body length (BL), head width (HW), hair length
(HL), inter-tegular distance (ITD) and fore-wing length (fWL). Life
history traits were also integrated via literature records, and then
assigned to MOTU. These included lecty (floral range), sociality,
parasitism and nest location (implicated in the utilization of resources).
Missing traits were predicted and assigned by modeling trait evolution
along branches of the phylogeny. A more comprehensive description of trait
prediction for phylogenetically placed MOTU can be found in Xie et al.
(2023).

# Tree diversity drives multiple facets of bee diversity via
microenvironment Dataset DOI:
[10.5061/dryad.zs7h44jjm](https://doi.org/10.5061/dryad.zs7h44jjm) ##
Description of the data and file structure ### Files and variables ####
File: diversity.csv **Description:** the diversity indices used in this
study ##### Variables * hill.shann: bee taxonomic diversity; estimation
using hill numbers (hereafter TD, ' q' = 1: Shannon diversity )
in the R package ‘hilldiv’ * PD.ses: standardized effect size of bee
phylogenetic diversity (Value~obs~ - Value~mean~) / Valuesd); Faith’s
phylogenetic diversity * FDiv.ses: standardized effect size of bee
functional diversity (Value~obs~ - Value~mean~) / Valuesd); Functional
divergence;  * Site: sampling site (A or B) * Plot: sampling plots * Year:
sampling year * Month: sampling month #### File: env.csv
**Description:** the environmental factors considered in this study #####
Variables * Site: sampling site (A or B) * Plot: sampling plots * Month:
sampling month * under_cover: cover of understory vegetation (%) *
canopy_cover: canopy cover (%) * MT: monthly temperature (℃) * MH: monthly
humidity (%) * TREE_R: tree species richness  (number of species)