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Partial shading by solar panels
delays bloom, increases oral
abundance during the late‑season
for pollinators in a dryland,
agrivoltaic ecosystem
Maggie Graham1*, Serkan Ates2, Andony P. Melathopoulos3, Andrew R. Moldenke4,
Sandra J. DeBano5, Lincoln R. Best3 & Chad W. Higgins1
Habitat for pollinators is declining worldwide, threatening the health of both wild and agricultural
ecosystems. Photovoltaic solar energy installation is booming, frequently near agricultural lands,
where the land underneath ground‑mounted photovoltaic panels is traditionally unused. Some
solar developers and agriculturalists in the United States are lling the solar understory with habitat
for pollinating insects in eorts to maximize land‑use eciency in agricultural lands. However, the
impact of the solar panel canopy on the understory pollinator‑plant community is unknown. Here we
investigated the eects of solar arrays on plant composition, bloom timing and foraging behavior
of pollinators from June to September (after peak bloom) in full shade plots and partial shade plots
under solar panels as well as in full sun plots (controls) outside of the solar panels. We found that
oral abundance increased and bloom timing was delayed in the partial shade plots, which has
the potential to benet late‑season foragers in water‑limited ecosystems. Pollinator abundance,
diversity, and richness were similar in full sun and partial shade plots, both greater than in full shade.
Pollinator‑ower visitation rates did not dier among treatments at this scale. This demonstrates
that pollinators will use habitat under solar arrays, despite variations in community structure across
shade gradients. We anticipate that these ndings will inform local farmers and solar developers who
manage solar understories, as well as agriculture and pollinator health advocates as they seek land for
pollinator habitat restoration in target areas.
Pollinating insects are a cornerstone of natural and agricultural ecosystems, aiding in the reproduction of 75%
of owering plant species1 and 35% of crop species globally2. In the US, pollination services to agriculture are
valued at $14 billion annually3. Habitat for pollinating insects is declining globally as a result of land use change,
attributed in part to urbanization, agricultural intensication, and general land development4.
Changes in global climate can also cause shis in habitat availability5. Global climate models predict increased
aridity globally as the climate warms, and increased uncertainty around seasonal drought patterns6, 7. ese
impacts are especially visible in dryland ecosystems, where photosynthetic production is water-limited (sunlight
is available in excess). Drylands account for 40% of land globally, and are dened by an Aridity Index (ratio
of precipitation to potential evapotranspiration) of less than 0.65. is includes deserts, as well as temperate
regions such as grasslands, savannahs, and Mediterranean ecosystems6–8. Changes in aridity, drought frequency,
and drought severity, can cause shis in temperature and that aect soil moisture, a key component of plant
growth7,9–11. Drought conditions can impact oral abundance and decrease the available forage for pollinators,
particularly later in the summer9–11.
OPEN
1Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR 97330,
USA. 2Department of Animal and Rangeland Sciences, Oregon State University, Corvallis, OR 97330,
USA. 3Department of Horticulture, Oregon State University, Corvallis, OR 97330, USA. 4Department of Botany and
Plant Pathology, Oregon State University, Corvallis, OR 97330, USA. 5Department of Fisheries and Wildlife, Oregon
State University, Hermiston Agricultural Research and Extension Center, Hermiston, OR 97838, USA. *email:
grahaann@oregonstate.edu
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Quality pollinator habitat requires access to soil, water, woody debris, and an abundance of nectar and pollen
producing plants across the entire foraging season of individual pollinators or colonies12. Given emergence times
and host-plant preference of pollinating insect taxa, habitat quality frequently depends on a diversity of ower-
ing species that span a range of bloom shapes and bloom timings13. Honey bee and native bumble bee wintering
success is strongly linked to nutrition, and in many regions of the Western US late-season bee taxa reproductive
success is dependent on the availability of late-blooming plants10,11,14.
Solar photovoltaic (PV) installation in the US has increased by an average of 48% per year over the past
decade, and current capacity is expected to double again over the next ve years15. PV can be installed on a vari-
ety of surfaces including built structures, open land, or water. Sizes can range from small, backyard residential
sites to multi-acre, utility-scale solar energy (USSE) systems. USSE installations can be a source of land cover
change, and can impact ecosystem services, such as biodiversity, when installed in natural areas16–22. USSE has
the potential to negatively impact biodiversity in wildland, desert ecosystems19, though impacts in temperate
drylands and former agricultural lands are understudied.
When large, vegetated land surfaces are used for PV installations (e.g. agricultural elds, deserts, rangelands),
the land is typically stripped of vegetation and graded20. A lower disturbance option exists to drill posts into the
ground, though heavy machinery is still used which compacts soils and disturbs vegetation. Aer construction,
the land is typically managed to limit plant growth since tall plants would block sunlight, decreasing energy gen-
eration. is management may include removing the existing vegetation, then covering with gravel or turf grass22.
Rarely is the understory space managed for ecological conservation or used as productive agricultural land.
Installations in areas already impacted by human development, such as existing rooops, parking lots, or
degraded lands, can minimize the conversion of undeveloped land in land-limited environments23, and options
exist for ecologically-synergistic, low-impact development24. One such option is agrivoltaics—a concept intro-
duced in the 1980s25 where solar energy production is combined with agricultural production (dual-use) on
the same land.
e concept of agrivoltaics has gained popularity in recent years as a means of creating low-impact solar
energy development in agricultural communities22. In the United States, solar developers have begun to utilize
the panel understory to promote both biodiversity and agricultural health by pairing PV with habitat for wild
and managed pollinators22,24. Some states, such as Minnesota, North Carolina, Maryland, Vermont, and Virginia,
have developed statewide guidelines and incentives to promote pollinator-focused solar installations22. In this
practice, forage for pollinators is established as the solar array’s understory rather than the traditional turf grass
or gravel. Some plantings focus exclusively on native species to prioritize restoration of native plant communities,
others include a mix of native and non-native species.
Despite a recent surge in pollinator-focused solar installations, little is known about how solar panel canopies
impact pollinators and the owers they forage. Recent studies document the response of desert plants to PV
in wildland ecosystems19,26, and crops such as pasture grasses27 and vegetables28–30 in agrivoltaic systems, yet
none have addressed oral density or insect populations. Panel shading alters sunlight and soil moisture levels,
creating a variety of microclimates within the solar understory18,19,21,25–31. Sunlight, water, and nutrients drive
plant growth, which then impacts oral abundance and timing32. Floral abundance and localized shading then
inuence pollinator community structure33–35. However, the relationships among panel shading, plants, and
pollinators have not been examined within a solar array.
To address this knowledge gap, we documented the species abundance, richness, and diversity of owers and
pollinators at a PV solar plant designed to provide habitat for pollinating insects and native plants. e objec-
tives of our study were to (1) determine if pollinators would visit owers in the solar array and (2) document
the species abundance, richness, diversity, and composition of insect pollinator and plant communities across
shade gradients (microclimates) within the solar array. We hypothesized that pollinators would visit owers
despite their location within the array, and that plant composition (as a result of species tolerance for shade and
temperature) as well as pollinator composition (as a result of species tolerance for shade, temperature, and oral
preference) would dier across shade gradients. Specically, we hypothesized that partial shading by solar panels
would create a microclimate that facilitates more abundant, more diverse owers and pollinators compared to
full sun (control) or full shade plots, particularly during the hot, dry months of July, August, and September.
Methods
Study location. We conducted this study at the Eagle Point Solar Plant in Jackson County, Oregon (42°24′
N, 122°50′ W; Fig.1). is 18 hectare (45 acre) site is located in the Rogue River Valley, west of the Cascade
Mountains, and east of the Oregon Coast Range, within the traditional land of the Takelma peoples (Fig.1a).
e Rogue Valley is a predominantly agricultural region. Popular crops include wine grapes, pears, and other
tree fruits. e site is bordered by agricultural elds (pears, hemp) and private residences. Permission to access
the site was granted by Pine Gate Renewables, LLC.
e site soils are composed of Coker clay (33A), Padigan Clay (139A), and Phoenix Clay (141A) soils, all
of which are Non-irrigated Class 4w soils36. At 412m (1350 ) of elevation, the site receives an average of 485
mm37 (19 in) of precipitation annually, and is considered a dryland, Mediterranean climate (2019 Aridity Index,
0.1638). e site is located in USDA plant hardiness zone 8b39.
In the fall of 2017, a 10MW AC (13MW DC) commercial solar generation facility was constructed on the
site. e array consists of monocrystalline panels mounted on 3m high racking with single axis tracking systems.
Light sensors in the trackers cause the panels to rotate, following the sun throughout the day. Rows of panels are
oriented along a north–south gradient, with panels tracking from east to west. Rows are spaced approximately
6m on center. At the steepest angle of rotation (early morning, late evening), the lowest edge of the panel is
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approximately 1m above the ground. When parallel with the ground (mid-day, sun overhead), the lowest edge
of the panel is approximately 3m above the ground.
Prior to solar development, the site was used primarily for cattle grazing40. e soils were highly compacted.
Site vegetation primarily consisted of non-native rhizomatous grasses40. Small numbers of native and non-native
forbs were also present at the site. Solar installation plans did not require massive grading, though some minor
grading was prescribed for the site access road. Installation plans aimed to preserve existing vegetation outside
of the required disturbance area. By nature of the installation process, some surface vegetation was removed, and
surface soils were disturbed in areas where solar panels were installed. Aer installation, the site was prepared
for restoration with native plants. In May 2018, clethodim was applied at 438ml/ha (6oz/ac) to portions of the
site already occupied by native forbs, the remainder of the site was treated with glyphosate, applied at the manu-
facturer recommended rate. Additionally, bindweed (Convolvulus arvensis) was spot sprayed with glyphosate in
June 2018. Manual removal of the highly invasive yellow starthistle (Centaurea solstitialis) occurred throughout
the site in 2018 and 2019. In October 2018, the site was restored with a mix of native forbs and grasses, with the
objective of providing habitat for both wild and managed pollinators40. e restoration species mix included a
variety of annual and perennial forbs (Supplemental Material), many grown from seed collected onsite or nearby.
Apart from Festuca roemeri, native grass species were not introduced during the initial planting to allow for
continued grass-specic herbicide use, but were planned for future installation. Ongoing maintenance at the site
consists of seasonal mowing, planting, and herbicide application, all part of the native plant restoration process.
e site is not grazed or tilled, thus ground disturbance is minimal post-construction. Minimal to no woody
debris is present on the site, though soil is abundant. A perennial water source is accessible to insects along the
Figure1. Site location and experimental design. e Eagle Point Solar Plant is (a) located in southern Oregon’s
Rogue Valley. We established (b) three replicates within the site, each with three treatments (full sun as yellow,
partial shade as green, full shade as blue), as shown in the (c) side view and (d) aerial view. Base imagery
sources: (a) Wikimedia Commons 2019, (b,d) Esri 2021, USDA FSA, GeoEye, Maxar.
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eastern edge of the property. An active apiary with 52 honeybee colonies is located along the southwest corner of
the site, within ight distance of all survey locations (Supplementary Figure S1). Additional colonies are located
on neighboring properties, and may be within ight range of the site.
Experimental design. We collected observational data on pollinator and plant populations during seven
sampling events in 2019, each spanning 2days (June 11–12, July 2–3, July 14–15, July 30–31, August 13–14,
August 27–28, and September 20–21). e study complied with all relevant institutional, national, and interna-
tional guidelines and legislation. Sampling events started aer peak bloom (late-April to mid-May) in early June,
and continued through late September (“late-season”). We established the survey as a complete randomized
block design with three replicates containing three 100 m2 treatment plots each (Fig.1). Shade intensity was the
treatment eect, and was determined by location within the solar array. Full shade (5% of total sunlight) plots
were located directly underneath solar panel rows (Fig.1c,d). Partial shade (75% of total sunlight) plots were
located between solar panel rows, with the middle of the plot centered between the pilings of adjacent solar panel
rows, which are approximately 6m on center. Full sun (100% of total sunlight) plots, which served as controls,
were located in open, unshaded areas still within the fenced property area (Fig.1c,d). Eort was made to place
full sun plots as close as possible to partial shade and full shade plots, with adjacent sides < 30m apart.
We selected replicate locations based on the availability of suitable full sun plots which we located within the
restored area, in areas not shaded by the solar panels (5m from an east or west edge, 3m from a north or south
edge, and greater than 100 m2 in area). e individual width to length ratio of the 100 m2 full sun plots varied
based on the conguration of available land and ongoing site maintenance activities (Supplementary Figure S1).
For example, we had to shi the edge of the full sun plot in block 3 mid-season aer a portion of the plot was
mowed by site maintenance sta. e block centroid (central point between adjacent sides of treatment plots)
for block 1 was located approximately 300m from that of block 2 and approximately 500m from block 3. e
block centroid of block 2 was located approximately 200m from that of block 3 (Fig.1b). Dierences among
replicates was expected (ex. distance to apiary, soil/slope dierences, etc.), which is why a complete randomized
block design was chosen for the study design.
We collected climate data at three monitoring stations to provide context for the study, separating meas-
urements by treatment when possible (Supplementary Figure S1). We collected net radiation (PYR Decagon
Devices), air temperature (VP-3 Decagon Devices), and relative humidity (VP-3 Decagon Devices) at 15min
intervals at a height of 1.4m. Soil moisture and soil temperature (GS-3 Decagon Devices) were also measured
at 15min intervals at a depth of 15cm.
We used the line point intercept method to inventory botanical composition in plots41. In each plot, 100 data
points were collected across ve, 2m transects at 10cm intervals. In full shade and partial shade plots, tran-
sects ran from north to south (parallel to panels), and were positioned in the center of the plot, either directly
underneath (full shade) or directly between (partial shade) rows of panels (Fig.1c,d). In full sun plots, transects
were in the center of rows 1.5m apart. We selected the starting point of transects at random before each sample
event. At each point intercept, we documented the species of the stem and the number of owers in bloom per
stem. Data points collected in each plot at each sampling event were added to determine a count of blooms per
100 m2 for each sample unit.
Flower morphology, notably the number and arrangement of inorescences in owers, varies between plants.
In this study, we are interested in the relative dierence between treatment plots, not individual species. We
dened “bloom” in a way that was practical for eld survey of each plant. For plants with distinct, unclustered
owers (e.g. Clarkia purpurea, Brodiaea elegans), we considered each ower a bloom unit (Fig.2a). For plants
with stems of clustered owers (e.g. Castilleja tenuis, Vicia americana, Brassica nigra, Dipsacus sp.), we consid-
ered individual owers a bloom unit (Fig.2b). For plants with distinct composite owers (e.g. Asteraceae), we
considered each capitulum a bloom unit (Fig.2c). For plants with owers composed of small, tight inorescences
(e.g. Daucus carota) it was not practical to distinguish between inorescences, so we considered each ower
head a bloom unit42 (Fig.2d).
We collected insect specimens to inventory pollinating insect composition in plots. We used hand nets to
survey insects visiting owers in each plot during a 30min sample event. We walked the plot continuously dur-
ing this time, observing insects in consecutive 1 m2 zones. Specimens collected in each plot during each sample
event were aggregated to determine a count of insects per 100 m2 per 30min for each sample unit.
We sampled continuously between 9 am and 4pm, on warm (> 16°C), calm (< 20km/h wind) days. Full
sun and partial sun plots were surveyed when plots were unshaded. Unshaded surveys were not possible in
full shade plots, which were surveyed when shaded. We collected all insects observed touching the reproduc-
tive parts of owers, excluding individuals from the family Miridae, which were found in large quantities on
stems, leaves, and owers of some plants. Aer netting, we placed insects in ethyl acetate jars and froze for later
identication. In the lab, we pinned, sexed, and identied specimens to species or the lowest taxonomic group
possible. Taxonomists (Dr. Andy Moldenke and Lincoln R. Best) conrmed identications and checked them
with voucher specimens at the Oregon State Arthropod Collection, at Oregon State University in Corvallis,
OR. An archived digital record of all specimens, including voucher material, is published to the Catalog of the
Oregon State Arthropod Collection43.
Statistical analysis. When conducting univariate analyses, we evaluated each sample unit (3 replicates × 3
treatment plots × 7 sample events = 63 total sample units) for dierences in species abundance, species richness,
species diversity, and visitation rate by performing a one-factor ANOVA (treatment) with repeated measures
(sample event) and a blocking factor (replicate). We used a paired t-test with a Bonferroni correction to make
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pairwise comparisons of means. We conducted all univariate analyses in R version 3.6.144 and used the vegan45
package to calculate species diversity. Our code is available in the Supplemental Material.
Before evaluating dierences in species abundance, we logarithmically transformed counts of both blooms
and insects (log(x + 1)) to improve normality and preserve extreme values46. We did not remove zero values (i.e.,
plots with no insects or no blooms), as these are important to the survey objectives. We dened species richness
as the number of unique types (species or lowest taxonomic group possible) of individuals in a given sample
unit46. We calculated species diversity for each sample unit using Shannon’s diversity index46. Visitation rate is
dened as the ratio of insect abundance per minute, adjusted for the density of blooms42. is estimates insect
use of oral resources relative to the number of resources available in each treatment, illuminating dierences
from factors other than oral density. We calculated visitation rate using (log (insects + 1)/(log (blooms + 1)) per
30min per sample unit. Units without any insects and/or any owers were assigned a value of zero.
For all univariate analyses, we evaluated the assumption of normality by plotting the quantiles of the model
residuals against the quantiles of a Chi-square distribution, also called a Q–Q scatterplot. We evaluated the
homogeneity of variances across treatments by creating box-whisker plots and conrming distribution was
relatively equal for each tested variable.
We preformed multivariate analyses using PC-ORD Soware version 7.0747. When conducting multivariate
analyses, we aggregated species abundances from sample units by month (June, July, August/September) to form
monthly sample units (3 replicates × 3 units × 3months = 27 sample units). We then aggregated species-level
abundances to higher taxonomic group-level abundances (Supplemental Material) to facilitate the analysis of
community trends. e bloom group dataset contained total blooms per month for each replicate and treatment
Figure2. Bloom units are dened by ower morphology. A bloom unit is considered an individual ower for
plants with (a) distinct, unclustered owers or (b) stems of clustered owers; a capitulum for plants with (c)
distinct, composite owers; and a ower head for plants with (d) multiple small, tight inorescences.
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(27 monthly sample units × 13 taxonomic groups). e insect group dataset contained total insects per month
for each replicate and treatment (27 monthly sample units × 13 taxonomic groups). e environmental dataset
contained experimental design variables (27 monthly sample units × 4 variables) such as replicate, treatment,
and month.
We used a nonmetric multidimensional scaling (NMS) ordination to compare the species community com-
position of monthly sample units. Ordination is a technique for summarizing complex, multivariate datasets,
which are common in community ecology46. In an ordination, data points are arranged on axis according to how
similar they are to each other. Points that are close on the graph are similar, points that are far are dissimilar46.
We conducted NMS with relative Sorensen distances, 250 random starts (slow and thorough), and did not penal-
ize ties. We used a randomization procedure to determine if solutions were more conclusive than expected by
chance (P values), and calculated the percent variance explained by the model axes (R2 values). We used Pearson
coecients to determine signicant (alpha = 0.05) relationships between taxa and ordination axes.
We used multiresponse permutation procedures (MRPPs) with relative Sorensen distances to evaluate the
signicance of dierences in morphological group composition between groups of treatments and months
(A-statistics, P values).
Results
Microclimate. Our unreplicated climate observations showed that solar panel shading alters the solar radia-
tion, soil temperature, soil moisture, and vapor pressure decit across treatments. From July to September, par-
tial shade plots received approximately 75% of the solar radiation received by full sun plots, equivalent to an
average of 3–4 fewer sun hours (roughly 10am to 4pm versus 8am to 8pm). e maximum radiation intensity
was comparable around midday in full sun and partial shade plots (Fig.3). In addition to reduced solar radia-
tion, partial shade areas experienced reduced soil temperature, elevated soil moisture, and reduced vapor pres-
sure decit when compared to full sun plots (Supplementary Figure S2). Full shade plots received approximately
5% of the solar radiation received by full sun plots, and never received maximum radiation intensity (Fig.3). In
addition, full shade plots experienced reduced further soil temperature and when compared to both full sun and
partial shade plots (Supplementary Figure S2). Soil moisture and vapor pressure decit data is not available for
full shade plots (Supplementary Figure S2).
Floral resources. Over the course of the study, we collected 6,300 vegetation data points from 48 species
of plants. Of these species, 26 were blooming at the time of survey. We counted a total of 6,543 bloom units on
owering stems. Floral abundance was greatest in partial shade plots, where we found 4% more blooms than in
full sun (p = 0.008) and 4% more than in full shade plots (p = 0.019, Fig.4a). Neither richness nor diversity of
owers diered among treatments (p = 0.11, p = 0.12 respectively). Floral abundance, richness, and diversity all
diered temporally across the seven sampling dates (p = 0.00135, p < 0.001, p = 0.01 respectively), but interaction
terms (sampling date × treatment) were not signicant (all p > 0.05).
e NMS ordination of sample units in plant species space produced a two-dimensional solution (nal
stress = 9.4, nal instability = 0, p = 0.004, cumulative R2 = 0.87) shown in Fig.5a,b. Axis 1 described 76.5% of
variation, axis 2 described 10.6%. Centroids for each treatment for each month are shown in Fig.5b. MRPP
described signicant dierences in plant community composition by month (A = 0.48, p < 0.001), but not treat-
ment (A = 0.02, p = 0.27). Vetch (Vicia sp.), buttercup (Ranunculus sp.), geranium (Geranium sp.), and other
sp. (Amsinckia sp.,Castilleja sp.,Achyrachaena sp., etc.)were negatively correlated with axis 1, implying an
association with plots sampled in earlier months. istles (Centaurea sp., Dipsacus sp.), tarweed (Madia spp.,
Hemizonia sp.), willowherb (Epilobium spp.) and lettuce (Lactuca spp.) were positively correlated with axis 1,
Figure3. Average daily ux in solar radiation across the agrivoltaic system. Indicated by dierent color, for
three treatments: full sun, partial shade and full shade.
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indicative of plots sampled in later months. Carrots (Daucus sp., Torilis sp.) were also negatively correlated with
axis 2, indicating association with shadier treatments. Tarweed (Madia spp., Hemizonia sp.), thistle (Centaurea
sp., Dipsacus sp.), chamomile (Anthemis sp.), and clarkia (Clarkia sp.) were positively correlated with axis 2,
indicative of sunnier treatments. Correlations with axes are available in Supplementary Material.
Treatment centroids were closest in June (indicating similarity), then diverged in July (indicating dissimi-
larity), only to reconverge in August/September. In July, the full sun centroid was close to the full sun August/
September centroid, indicating similar species composition. Meanwhile the July centroids for partial shade and
full shade were distant from the August/September centroids, indicating dissimilarity. is illustrates that full
sun plots transitioned to the late-summer plant community, characterized by Madia sp, Hemizonia sp., Lactuca
sp., and Epilobium sp., before full shade or partial shade plots, indicating a delay in bloom timing.
Pollinating insects. We collected 342 pollinating insects over the course of the study, representing 65 dif-
ferent insect species. Of these individuals, 45% were native bees, 20% were ies (Diptera spp.), 12% were honey
bees (Apis mellifera), 12% were beetles (Coleoptera spp.), and 7% were wasps (other Hymenoptera spp.), and
3% were from other taxonomic groups (Lepidoptera, Hemiptera; Fig.6). e native bee speciemns represented
20 dierent species, including species from the genera Bombus (bumble bee), Ceratina (small carpenter bee),
Eucera, (longhorn bee) Halictus (sweat bee), Lasioglossum (sweat bee), Megachile (leafcutter bee), Melissodes
(longhorn bee), and Osmia (mason bee). We found an average of 3% more pollinating insects per 100 m2 in
partial shade and full sun plots than in full shade plots (p < 0.001, p < 0.001 respectively, Fig.4b). Insect species
richness was higher in partial shade and full sun than in full shade (p < 0.001, p < 0.001 respectively; Fig.4c), as
was species diversity (p = 0.001, p < 0.001 respectively; Fig.4d). Species diversity also varied by time (p = 0.011)
though interaction terms were not signicant. Insect to ower visitation rates did not dier between treatment
plots at this scale (p = 0.184).
e NMS ordination of sample units in insect species space produced a three-dimensional solution (nal
stress = 7.4, nal instability = 0, p = 0.012, cumulative R2 = 0.85) shown in Fig.7a–d. Axis 1 described 42% of
Figure4. Plant and pollinator community populations over time by measurement type: (a) bloom abundance,
(b) insect abundance, (c) insect richness, and (d) insect diversity. Each symbol represents the mean of 3
observed values, indicated by dierent color, for three treatments: (1) full sun (2) partial shade, and (3) full
shade.
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variation, axis 2 described 21%, and axis 3 described 22%. Centroids for each treatment for each month are shown
in Fig.7b. MRPP described signicant dierences in community composition by month (A = 0.24, p < 0.001), but
not treatment (A = 0.034, p = 0.13). Examination of axes 1 and 2 shows Bombus spp., Osmia spp., and other spp.
(Hemiptera, Lepidoptera) were more common in plots sampled in June, particularly in the partial shade. Halictus
Figure5. (a) Plant community composition described through a nonmetric multidimensional scaling of
sample units (averaged by month) in insect species space, with weighted average positions shown for species
signicantly correlated with axes. Sample units that are close together in the graph are more similar (in species
composition) than those that are farther apart. Convex hulls connect groups of treatments (by month). Colored,
un-lled symbols represent sample units. Black circles represent species. Joint-plot vectors (red lines) show
environmental variables correlated with the axes, vector length represents correlation strength. (b) Successional
vectors connect centroids from each group of treatments (by month) to illustrate community change over
time. Colored, lled symbols represent centroids. Black circles represent species. Fundamental coordinates
were generated and data exploration was conducted in PC-ORD Soware version 7.0747. Figure linework and
aesthetics were created in Microso PowerPoint 2016.
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spp. and Lasioglossum spp. were common in plots sampled in July, August, and September (Fig.7a,b). On axis 2
and 3, we see that Bombus spp. and Diptera spp. were common in the full shade and partial shade during June,
while Apis mellifera and wasps were characteristic of full sun plots in June and July (Fig.7c,d). Correlations of
taxonomic groups with axes are available in the Supplementary Material. Along axes 1 and 2, the centroids for full
sun and partial shade plots followed a similar trajectory through insect space, and become closer (more similar)
as time progressed. In contrast, the centroids for full shade followed a dierent trajectory and are farther away
from the full sun and partial shade plots, indicating dissimilarity (Fig.7a,b). Along axis 3, partial shade plots
Figure6. Percentage of pollinating insects contributed by dierent taxonomic groups, indicated by color.
Figure7. Insect community composition described through a nonmetric multidimensional scaling of
sample units (averaged by month) in insect species space, with weighted average positions shown for species
signicantly correlated with (a,b) axes 1 and 2 and (c,d) axes 1 and 3. Sample units that are close together in
the graph are more similar (in species composition) than those far apart. (a,c) Convex hulls connect groups
of treatments (by month). Colored, un-lled symbols represent sample units. Black circles represent species.
Joint-plot vectors (red lines) show environmental variables correlated with the axes, vector length represents
correlation strength. (b,d) Successional vectors connect centroids from each group of treatments (by month)
to illustrate community change over time. Colored, lled symbols represent centroids. Black circles represent
species. Fundamental coordinates were generated and data exploration was conducted in PC-ORD Soware
version 7.0747. Figure linework and aesthetics were created in Microso PowerPoint 2016.
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appear more similar to full shade than to full sun. All three treatments are characterized by Apis mellifera in July,
the move to communities with more Diptera spp. in August and September (Fig.7c,d).
Discussion
e dierences in oral abundance, and delay in bloom timing that we observed among treatments in this experi-
ment demonstrate that microclimates created by solar panel shading impact plant physiology and morphology,
and shed light on how plants might respond to partial shade conditions under solar panelsduring times of
drought. Other researchers have documented changes in plant phenology due to solar panel microclimates in
dryland ecosystems at sites with dierent climates, panel arrangements, and local soil conditions. Adeh etal. 27
found elevated biomass of forage grasses in full shade microclimates27. Hernandez etal. 26 documented increased
seedset of desert annuals and perennials in full shade microclimates26. What aspects of plant phenology change,
and how they change, may depend on individual plant preferences for temperature, moisture, and sunlight.
Angiosperms have evolved strategies to alter bloom time, length and intensity in response to environmental
conditions (light, temperature, moisture)9,48–52. e eects of shading on owering depend on the individual
species preferences and local growing conditions. Zhao etal.51 found that growing herbaceous peony (Paeonia lac-
tiora) owers in a shaded environment caused declines in key sugars and proteins, which delayed and prolonged
owering51. ey also observed a decrease in fresh ower weight and an increase in ower diameter, indicating a
change in resource allocation as a result of shading51. When examining shaded and unshaded coee plantations,
Prado etal.52 also observed dierences in ower morphology, but did not see a dierence in nectar or pollen
levels, which are key drivers of pollinating insect populations52. e increased oral abundance and delayed
bloom timing that we observed in the partial shade (versus full sun) could be the result of reduced sun hours on
photoperiodicity48,49, photosynthetic eciency51, or transpiration eciency53; the decrease in soil temperature
and moisture on germination49, root establishment9; or a combination of these strategies and mechanisms. us
when planting solar arrays with owering plants, land managers may expect to see dierences in bloom timing
and abundance along shade gradients. At our site, partial shading by solar panels increased bloom abundance by
delaying bloom timing, increasing forage for pollinators during the hot, dry, late-season—a time when nutrition
is particularly important.Which area (ex. full shade, partial shade, full sun) produces the most blooms may vary
based on climate, panel design, and local site conditions.
We observed dierences in the abundance, richness, diversity of the pollinator community along shade vari-
ations within the solar array, but the lack of a signicant correlation between treatment and the ordination axes
indicates that there was too much variation in the data to draw conclusions about species specic trends with
regard to treatment. Dierences in shading may facilitate niche-partitioning as a result of species tolerances for
shade, temperature, and oral preference, but more study is needed to show which treatments favor particular
insect taxonomic groups.
Since visitation rates did not dier among treatments, but oral abundance did dier among treatments,
variations in the pollinator community can be partially attributed to the high variation in the plant commu-
nity documented at this scale. ere may be additional environmental or biological factors (e.g. temperature,
wind, pests) impacting the pollinator community within treatments. While we measured temperature before
each survey, our measurements were not a scale ne enough to make inferences. Generally, pollinators prefer
foraging in sunny rather than shady conditions34, although shadier regions may be preferred by some taxa (ex.
bumblebees, ies) that have the capacity to forage at lower temperatures54. While full sun and partial shade plots
were surveyed when plots were sunny, this was not possible in full shade plots, which were actively shaded at the
time of survey. us active shading likely resulted in lower ambient air temperatures, which could have aected
pollinator populations in addition to variations attributed to shade-grown owers. Even though abundance,
richness and diversity were less in full shade than in either partial shade or full shade plots, we still observed
pollinators foraging on owers, and visitation rates were not statistically dierent at this scale. Future studies
may want examine whether pollinators use shade corridors as yways in addition to pollen and nectar foraging.
Our unreplicated climate observations from partial shade and full sun plots were generally consistent with
observations by Barron-Gaord etal.29, Adeh etal.27, and Marrou etal.28, though the magnitude of these meas-
urements varies with panel arrangement, latitude, and time of year. Unfortunately, we are not able to compare
our full shade measurements due to possible equipment issues. Replication of all climatic measurements would
have improved our ability to interpret these observations, but was outside the scope of this study.Whether or
not microclimatic variations are benecial to plant and insect populations depends largely on specic plant
characteristics and local climate. Whether the ecosystem is water-limited (dryland) or light-limited (surplus of
water) may inuence how plants react to partial shading by solar panels.
When properly sited, pollinator-focused solar provides an opportunity for solar energy development to benet
rather than degrade biodiversity, as has been documented in developments in wildlands of southern California19.
When placed in areas with high ecosystem services, solar development can negatively impact ecosystem services
such as biodiversity19. When placed in areas of low ecosystem services, pollinator-focused solar has the potential
to positively impact ecosystem services such as biodiversity, and pollination services through the creation of pol-
linator habitat and restoration of native plants species 22,24,55. Agricultural areas in themselves can also promote
high biodiversity56, so land use tradeos should be evaluated on a case-by-case basis.
Additionally, increases in pollinator biodiversity near agricultural lands could increase pollination services to
agriculture, which has the potential to increase crop yields and prots22. Whether pollinator habitat collocated
with solar is more biodiverse or provides more pollination services than pollinator habitat not collocated with
solar is unknown, and provides another avenue for future study. Future research should examine the impacts
of solar, biodiversity, and agricultural yields on a landscape scale to determine whether benets are realized.
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Inferences. Observations of species-based performance (ex. which treatment produced the most owers
and of what composition) are not transferable to sites with diering climates, species mixes, and panel arrange-
ments; however, the general trends of the data—that plant communities vary with shade—are consistent with
physical mechanisms and prior botanical studies9,32,48,49,53. us, we can expect both plant and pollinators com-
munities to vary along shading gradients throughout solar arrays, and pollinators to visit owers despite their
proximity to solar panels. We expect that visitation rate will not dier and that oral abundance, bloom timing,
insect abundance, insect richness, and insect diversity may vary, following characteristics of the local climate,
species mix, and solar panel design.
Conclusion
Our results show that (1) pollinating insects visited owers regardless of the presence of solar panels, and (2)
that shading from solar panels altered the abundance and timing of oral blooms visited by pollinators, and
inuenced the abundance, richness and diversity of the pollinator community. us, planting solar arrays with
pollen and nectar producing plants (owers) creates habitat for pollinating insects, and "pollinator-friendly" solar
installations should include multiple plant species that are shade-tolerant or thrive in full sun to maximize the
niche-partitioning inherent in insect pollinator communities. Microclimates with partial shading may provide
additional benets in drylands during hot, dry summers. Unused or underutilized lands below solar panels rep-
resent an opportunity to augment current paucity and expected decline of pollinator habitat. Near agricultural
lands, this also has the potential to benet the surrounding agricultural community. Solar developers, policy
makers, agricultural communities and pollinator health advocates looking to maximize land use eciency, bio-
diversity, and pollination services may consider pollinator habitat at solar photovoltaic sites a viable pathway,
while evaluating specic considerations, such as local climate and current land-use, on a case-by-case basis.
Received: 20 October 2020; Accepted: 18 March 2021
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Acknowledgements
is research was supported in part by the Agricultural Research Foundation of Oregon State University and NSF
Grant #1740082. e authors would like to thank the sta and volunteers of the Oregon Bee Project for assistance
with insect taxonomy, Sean and Kathryn Prive for botanical expertise, John Jacob for honey bee expertise, Mary
Alice Coulter for assistance with specimen collection and database management, and both Pine Gate Renewables
and 1000 Friends of Oregon for their assistance in nding and accessing this study site.
Author contributions
M.G., S.A., A.P.M., and C.W.H. developed ideas, designed methodology. M.G., S.A., and S.J.D. conducted sta-
tistical analysis. A.R.M., L.R.B., and M.G. identied insect specimens. M.G. established research sites, collected
data, and led writing of the manuscript. All authors reviewed the results and contributed to the writing of the
manuscript.
Competing interests
MG is employed by both Oregon State University and e Understory Initiative. e authors declare no other
potential competing interests.
Additional information
Supplementary Information e online version contains supplementary material available at https:// doi. org/
10. 1038/ s41598- 021- 86756-4.
Correspondence and requests for materials should be addressed to M.G.
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