Enhancing Pollinator Habitat in Pacific Northwest Croplands Using DNA Metabarcoding Techniques

Progress report for GW19-188

Project Type: Graduate Student
Funds awarded in 2019: $25,000.00
Projected End Date: 06/30/2021
Grant Recipient: Oregon State University
Region: Western
State: Oregon
Graduate Student:
Major Professor:
Dr. Sandy DeBano
Oregon State University
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Project Information


As the global population continues to expand, there is a need for increased food production. Over 35% of the world’s food supply is animal-pollinated, and, therefore, there is also an increasing need for pollination services. However, many pollinator populations, including those of native bees, have been decreasing world-wide. Producers are quickly becoming more aware of the benefits associated with enhancing native bee habitat on and around their croplands. However, one of the most significant challenges that producers face is uncertainty about which species to plant to enhance crop pollination. Most pollinator-friendly plant recommendations are based on anecdotal evidence and are not tailored for particular regions, bee communities, or crop phenologies. More research on pollinator foraging is needed due to a lack of reliable empirical data on which plants are best for supporting native bees in crop production areas. Recent developments in molecular ecology provide a novel solution to this problem: DNA metabarcoding. The goal of this project is to use DNA metabarcoding on pollen collected from foraging bees to identify plant species that serve as important food sources for native bees on and around croplands in the Pacific Northwest (PNW). This method is less time consuming and requires less expertise than traditional methods for describing plant-pollinator interactions (e.g., field observations and light microscopy). DNA metabarcoding techniques yield higher taxonomic resolution and provide a more complete record of plant use during a foraging bout relative to visual observations. Once we have identified preferred plant species, we will disseminate this information to producers and other land managers in the Pacific Northwest through scientific publications, presentations, outreach events, and an extension publication providing specific guidance. These data and outreach products will help producers in the PNW develop sustainable agriculture practices that will ultimately enhance native bee habitat and increase crop yields and profits.

Project Objectives:

The objectives of this study are to:


  1. document flower-native bee associations in agroecosystems of eastern Oregon using both behavioral observations and DNA metabarcoding in June-August 2019;
  2. identify non-crop plant species that serve as important food sources for native bees by comparing each plant species availability in the environment with its use by native bees by the end of the first year of the project; and
  3. disseminate the results of the study to producers currently engaged in native bee habitat projects on their farms as well as other interested parties through various outlets during the second year of the project.  


Click linked name(s) to expand
  • Martin Pitney - Producer


Materials and methods:

Study Sites

The field component of the project was conducted in 2018 and 2019 at three locations in eastern Oregon, US: Threemile Canyon Farms (45.7513° N, 119.9376° W), the United States Forest Service (USFS) Starkey Experimental Forest and Range (45.2332° N, 118.5511° W), and The Nature Conservancy’s (TNC) Zumwalt Prairie Preserve (45.5559° N, 116.9587° W). These locations were chosen with the goal of examining plant-bee associations in different land use types (i.e., agroecosystem, riparian forest, and grassland respectively). The three study sites occur within 350 km of each other.

Threemile Canyon Farms (Threemile) is a 37,636 ha industrial, pivot-irrigated farm located in Morrow County (elevation 100-300 m). Threemile has 9,308 ha in conservation area, 15,985 ha of irrigated conventionally managed cropland, and 6,178 ha of irrigated organically managed cropland. Uncultivated habitat includes the conservation area, which consists of arid grassland and shrub-steppe, as well as field margins, which are dominated by non-native vegetation. The Starkey Experimental Forest and Range (Starkey) is located in Union County (elevation 1,130-1,500 m); this long-term research site established in 1940 is grazed by cattle under a standard USFS grazing permit, and also supports herds of deer (Odocoileus spp.) and elk (Cervus canadensis). TNC’s Zumwalt Prairie Preserve (Zumwalt) is a 13,269 ha remnant bunchgrass prairie in Wallowa County (elevation 1,100-1,700 m). The Zumwalt Prairie has been used as summer pasture for horse, sheep, and cattle for over 100 years, but the majority of the area remains dominated by native plant species.

Bee Sampling

Bees were sampled during peak foraging hours (0900-1800) once a month from each site during June, July, and August of each year. Each bee was caught directly in a glass vial, if possible, or with an insect net and placed in an individual glass vial. All vials and nets were placed in 10% bleach for at least one minute and dried prior to sampling. Nets were replaced with clean, bleached nets after each use to prevent pollen contamination among samples. No killing agent was used in the vials, and bees were frozen at the end of each field day. Vials were labeled with the time, date, location, and flower species that the bee was foraging on when it was collected. The flower species noted for each bee was considered the bee foraging observation for that specimen. Each bee was given a unique sample ID so that it could later be associated with its specific pollen load. Bees were pinned, labeled, sexed and identified to the lowest taxonomic level possible, usually species.

Pollen Isolation

Bees were washed in the glass vial in which they were collected in the field. Sterilized water was added to the vial, and the vial was shaken vigorously until all visible pollen was removed from the bee. The pollen solution was pipetted from the vial and transferred to a 50 mL centrifuge tube. If pollen was still visible on the bee or the walls of the vial, it was rinsed with additional sterilized water, shaken vigorously, and the solution was pipetted from the vial and transferred to the same 50 mL centrifuge tube. The bees were then stored in a freezer until they could be pinned and identified. Each tube containing an individual bee’s pollen load was centrifuged at 2,000 rpm for 2 min, and the supernatant was discarded. The pollen pellet was resuspended in 1 mL of water, and the solution was transferred to a 1.5 mL screw cap microcentrifuge tube. The screw cap tubes were centrifuged at 12,500 rpm for 30 s and the supernatant was discarded. The pollen pellet was washed in 1 mL of 100% ethanol, and the supernatant was discarded. The pollen pellet was then dried for 30 min in an Eppendorf vacufuge. Dried pollen pellets were stored at -20°C until further processing.

DNA Extraction and PCR

DNA extractions took place using the Macherey-Nagel Nucleospin Food kit (Macherey-Nagel, Bethlehem, Pennsylvania, USA). We followed the “isolation of genomic DNA from honey or pollen” supplementary protocol. We added 1 mm glass beads to each 1.5 mL screw cap microcentrifuge tube and homogenized the samples in a Mini-BeadBeater-24 (Biospec Products Bartlesville, Oklahoma, USA). We included negative controls with each round of DNA extraction using sterilized water instead of pollen.

We used the dual-indexing pollen DNA metabarcoding strategy. We used the second internal transcribed spacer (ITS2) primers ITS S2F and ITS4R and existing universal ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL) primers, rbcL2 and rbcLaR. Illumina overhang adapter sequences were added to each of the primers. The polymerase chain reaction (PCR) was conducted as a multiplex, targeting both sequence regions for amplification in one reaction. The PCR reactions contained 10 μL of 5X Green GoTaq® Reaction Buffer, 0.3 uL of 10 mM dNTPs, 1 uL of each primer, 0.2 uL of GoTaq® DNA Polymerase, 33.5 uL of water, and 2 uL of the template DNA for a total volume of 50 uL per reaction. PCR cycles included an initial period of heat activation for 3 min at 95°C. This was followed by 35 cycles of 30 s at 95°C, 30 s at 55°C, and 1 min at 72°C. There was a final extension of 10 min at 72°C and then the samples were held at 10°C until further processing. A negative control was included in each round of PCR, where 2 uL of water were used instead of template DNA. A 5 uL sample of each PCR product was electrophoresed in a 2% agarose gel, stained with GelRedTM (Biotium Inc, Fremont, CA), and visualized under UV light to confirm the presence of the appropriately-sized amplicons. The PCR products were purified using the Promega Wizard SV Gel and PCR Clean-Up System. DNA was quantified for each sample using a Nanodrop 2000 Spectrophotometer (Thermo Scientific), and the samples were diluted with sterilized water in 96-well plates. The samples were indexed, pooled, and sequenced at the Center for Genome Research and Biocomputing at Oregon State University in a standard flow cell v3 300bp paired-end full service run of the Illumina MiSeq instrument.


Bioinformatics- Read Quality Filtering and Denoising

Raw reads were retrieved across all samples for ITS2 and rbcL, respectively. The open-source QIIME2 pipeline (version 2019-04) was used to analyze species composition of pollen loads. Initially, the QIIME2 DADA2 plugin was used to filter read quality and denoise reads. Sequence reads with a median sequencing quality score below 22 and noisy reads were filtered out. Contigs were created by joining paired-end reads, and duplicate sequences and sequences with chimera were removed. The resulting feature table containing optimized sequences and amplicon sequence variants (ASV) was used for additional analyses (e.g., taxonomic classification).

Bioinformatics- Reference Database Construction

Lists of all plant species known to occur at the three study sites have been developed over multiple years by botanists conducting field work at each site. A regional plant list was developed that included the unique species of each study site. Reference sequences of the ITS2 region and rbcL gene were extracted from publicly available data). A python package, NCBI-Companion (https://github.com/lixiaopi1985/NCBI_Companion), was used to query the NCBI nucleotide database to obtain sequences of species that were not present in published datasets. ITSx and MetaCurator software were used to detect and extract ITS2 and rbcL regions in the NCBI queried reference sequences, respectively. The final “regional” ITS2 and rbcL database was then split into three smaller “local” databases for ITS2 and rbcL that contained only species known to occur at each individual site.

Bioinformatics- Training Classifier and Taxonomy Classification

The Naïve Bayes algorithm provided by the QIIME2 feature-classifier plugin was used to train a classifier for taxonomic assignment based on the ITS2 and rbcL databases. Initially, the default settings were applied with high accuracy and low recall. In one case, taxonomic assignment of the classifier resulted in most of the samples being unassigned, so the kmer length was increased and confidence threshold reduced (high recall kmer = 32; confidence threshold = 0.6) to increase taxonomic assignments. The classify-sklearn tool of the QIIME2 feature-classifier plugin was used to assign taxonomy to each ASV.

Sequence Count Removal Threshold

Using the average and standard deviation of the number of sequencing reads in all negative control samples, we added 1.645 standard deviations to the average and used this number as our sequence count removal threshold. This allowed us to account for 95% of possible “background noise” detected in the pollen samples. We set the sequence count to zero for any taxonomic assignment whose read count fell below the sequence count removal threshold.

Network Analyses

Bipartite plant-pollinator networks were created, and network statistics were calculated using the bipartite package in R. Plant-pollinator networks were based on bee foraging observations and plant species assignments derived from DNA metabarcoding data using reference libraries of regional (MB-RDB) and local (MB-LDB) plant species. For each location, data from all sampling periods were combined into one network for each detection method (i.e., bee foraging observations, MB-RDB, and MB-LDB). Analysis of variance (ANOVA), blocked by location, was used to compare four parameters of pollinator networks: connectance (the realized proportion of possible links), H2’ (a frequency based index that increases with greater ecological specialization), linkage density (the mean number of links per species within the network), and bee generality (the mean number of plant species visited by each pollinator species within the network). Network parameter values were scaled to the highest value for a given parameters at a given site prior to analysis to account for differences in location. Tukey’s honest significant difference test (HSD) was used to compare means among detection methods. Analyses were conducted using R, version 4.0.0.

Research results and discussion:

Each sampling location was characterized by a distinctive group of bees interacting with varied plant communities. At Threemile Canyon Farms, the 139 bees used in the network analyses included 14 genera and 22 species. Anthophora curta was the most abundant species, making up 24% of bees sampled, and was observed visiting four plant species. Other common bee species included Agapostemon femoratus, Melissodes bimatris, and Melissodes pallidisignatus. Pollen loads of bees sampled from Threemile contained an average of 1.6 plant species using MB-RDB and an average of 2.1 plant species using MB-LDB.

At Starkey, the 168 bees used in the network analyses included 12 genera and 39 species. Bombus bifarius was the most commonly sampled species, making up 21% of bees sampled, and was observed visiting 12 plant species. Other common species included Bombus flavifrons, Halictus ligatus, and Bombus californicus. Pollen loads of bees sampled from Starkey contained an average of 2.0 and 2.4 plant species identified using MB-RDB and MB-LDB respectively.

At Zumwalt, the 96 bees used in the network analyses included 9 genera and 35 species. Bombus californicus and Bombus centralis were the most commonly sampled species, making up 17% and 13% of bees sampled respectively. B. californicus was observed visiting 8 plant species, and B. centralis was observed visiting 6 plant species. Pollen loads of bees sampled from Zumwalt contained an average of 1.7 plant species identified using MB-RDB and MB-LDB.

When using taxonomic assignments from ITS2 and rbcL combined, MB-RDB and MB-LDB detected and assigned more plant species than bee foraging observations at all locations. Plant-pollinator networks created from MB-RDB and MB-LDB were more complex than those created from bee foraging observations. Mean connectance, linkage density, bee generality and H2’ of networks produced by the three detection methods were statistically different. When compared to networks based on bee foraging observations and networks produced from MB-RDB, networks produced from MB-LDB had significantly higher connectance (F = 14.6, p = 0.005). Networks produced from MB-RDB and MB-LDB had significantly higher linkage density (F = 15.5, p = 0.004), and bee generality (F = 14.6, p = 0.005) than networks based on bee foraging observations. Specialization (H2’) was significantly higher for networks produced from bee foraging observations compared to those produced by MB-RDB and MB-LDB (F = 16.3, p = 0.004). The mean number of plant species per pollen load identified through observation was significantly lower than the mean number of plant species per pollen load identified by MB-RDB and MB-LDB (F = 16.3, p = 0.004).

Comparing Plant Species Assignments Obtained Using MB-RDB and MB-LDB

Of the ten plant species detected in the greatest number of pollen loads using each detection method, MB-RDB and MB-LDB assigned 60%, 50% and 90% of the same species at Threemile, Starkey, and Zumwalt respectively. At all locations, MB-RDB and MB-LDB assigned 78.6% of plant families and 61.2% of plant genera that bees were visiting when sampled. At Threemile and Starkey, we found that using the local database to assign taxonomy resulted in a higher percentage of detections of the plant species that bees were visiting when sampled relative to the regional database. At the Zumwalt sites, lower percentages of the plant species that bees were visiting when sampled were detected using MB-LDB relative to the other two sites.

In this study, we define a regional mismatch as a plant species that was identified using MB-RDB but is not known to occur in the local area of interest. We determined that 20%, 10%, and 9% of the plant species identified using MB-RDB at Threemile, Starkey, and Zumwalt respectively were regional mismatches. The use of the local database eliminated the potential for regional mismatches.

Inconsistencies Among Bee Foraging Observations and DNA Metabarcoding Data

At Threemile, three of the nine plant species that bees were observed visiting made up 81% of observations: diffuse knapweed (Centaurea diffusa Lam.) (41%), hoary tansyaster (Machaeranthera canescens [Pursh] A. Gray ssp. canescens var. canescens) (24%), and yellow star-thistle (Centaurea solstitialis L.) (16%). While both metabarcoding approaches detected yellow star-thistle in a large proportion of pollen loads, diffuse knapweed was not detected by MB-RDB, and hoary tansyaster was not detected by MB-LDB.

Of the 26 plant species that bees were observed visiting at Starkey, only three made up 51% of observations: slender cinquefoil (Potentilla gracilis Douglas ex Hook.) (22%), Missouri goldenrod (Solidago missouriensis Nutt.) (20%), and mountain monardella (Monardella odoratissima Benth.) (9%). Slender cinquefoil was not detected above the sequence count removal threshold using MB-RDB. Mountain monardella was detected with the rbcL gene using both MB-RDB and MB-LDB. However, it was not included in the ten plant species that were most commonly detected in the pollen samples for MB-RDB or MB-LDB. Furthermore, Missouri goldenrod was not detected using MB-RDB. At Starkey, seven plant species that bees were observed visiting were not identified using plant species assignments derived from DNA metabarcoding data. Of these, only one species (largeflower triteleia [Triteleia grandiflora Lindl.]) was missing ITS2 sequence data from the reference library and therefore pollen sequences could not possibly be assigned based on the ITS2 region.

Of the 23 plant species that bees were observed visiting at Zumwalt, four made up 40.6% of behavioral observations: silky lupine (Lupinus sericeus Pursh) (10.4%), whitestem frasera (Frasera albicaulis Douglas ex Griseb.) (10.4%), shaggy fleabane (Erigeron pumilus Nutt.) (10.4%), and slender cinquefoil (9.4%). While whitestem frasera was identified by both the ITS2 region and rbcL gene using the regional and local reference libraries, slender cinquefoil, shaggy fleabane, and silky lupine were not detected using either MB-RDB or MB-LDB. At Zumwalt, 14 plant species that bees were observed visiting were not detected by DNA metabarcoding. Of these, three species were missing ITS2 and rbcL sequence data from the reference library, making species assignment impossible.

Although additional analyses are currently underway, we have successfully documented flower-native bee associations in agroecosystems of eastern Oregon using both behavioral observations and DNA metabarcoding. These results have allowed us to identify several non-crop plant species that serve as important food sources for native bees, and have shown that using metabarcoding approaches on pollen collected from foraging bees provide a more complete understanding of native bee foraging behavior in eastern Oregon and a better understanding of which plants support those bees.

Participation Summary
1 Farmer participating in research

Educational & Outreach Activities

2 Journal articles
16 Webinars / talks / presentations

Participation Summary

30 Farmers
40 Ag professionals participated
Education/outreach description:

The project team has engaged in numerous educational and outreach activities that have presented progress and results of the metabarcoding project in 2019 and 2020, including the following:

 [“(KA)” indicates Katie Arstingstall was presenter, “(SJD)” indicates Sandra DeBano was presenter]

Invited Presentations


  • (SJD) At Home on the Range: Examining Native Bee Diversity and Ecology in Eastern Oregon. Wide World of Bees – Stay-at-Home Lectures Webinar Series.
  • (SJD) Native Bees in Pacific Northwest Rangelands: Challenges in Management and Conservation. Society for Range Management Annual Meeting, Denver, Colorado.


  • (SJD) Managing Forested Riparian Areas for Pollinators: Effects of Ungulate Herbivory and Restoration on Native Bees. Entomological Society of America Meetings, St. Louis, Missouri.
  • (SJD) Native Bee Research at the USFS Starkey Experimental Forest and Range. Interagency Pollinator Working Group, Washington DC.
  • (SJD) Native Bees, Riparian Restoration and Ungulate Herbivory at the US Forest Service Starkey Experimental Forest and Range. Western Forest Insect Work Conference, Anchorage, Alaska.
  • (KA) Comparing Behavioral Observations with DNA Metabarcoding Techniques for Identifying Major Food Sources for Native Bees in Eastern Oregon. Oregon Chapter of the Wildlife Society Annual Meeting, Bend, Oregon.
  • (SJD) Native Bees in Managed Ecosystems of the Interior Pacific Northwest: Research and Extension Efforts at the OSU Invertebrate Ecology Laboratory. PNW Pollinator Summer and Conference, Corvallis, Oregon.
  • (SJD) Native Bees of the Zumwalt Prairie: A Decade of Discovery. OSU/EOU Range Club, La Grande, Oregon.


National and International Presentations


  • (KA) Comparing Behavioral Observations with DNA Metabarcoding Techniques for Identifying Major Food Sources for Native Bees in Eastern Oregon, Annual Meeting of the Ecological Society of America, Louisville, Kentucky.
  • (KA) Plant-Pollinator Networks Created from DNA Metabarcoding Data in Eastern Oregon are More Complex than Those Created from Behavioral Observations. International Conference on Pollinator Biology Health and Policy, University of California, Davis, CA.


Extension Presentations


  • (KA) Unlocking the Mysteries of Bee Foraging Behavior with DNA. Farm Fair, Hermiston, Oregon. Audience: ~40 growers, fieldmen, chemical representatives, scientists, and the general public.
  • (SJD) Recent Advances in Understanding Human Influences of Native Bees in Eastern Oregon. Farm Fair, Hermiston, Oregon. Audience: ~40 growers, fieldmen, chemical representatives, scientists, and the general public.


Educational Outreach


  • (SJD) Guest lecture on “What’s the Buzz? Bees – Their Importance and Protection” for Blue Mountain Community College Course CSS 240: Pest Management.
  • (SJD) Guest lecture on “Native Bees – Current Research at The Nature Conservancy’s Zumwalt Prairie Preserve” for Eastern Oregon University’s RNG 241: Rangeland Ecology and Management.


  • (SJD) The Influence of Disturbance on Native Pollinators in Eastern Oregon: A Decade of Discovery. High Desert Museum, Pub Talk, Bend, Oregon.
  • (KE) Guest lecture on “The Role of Bees in Sustainable Agriculture – High Tech Approaches for Enhancing Pollinator Habitat” for Blue Mountain Community College Course CSS 240: Pest Management.

The project team had hoped to present the results of the project at the OSU HAREC grass, wheat, and field days in May and June 2020, but because of the Covid-19 pandemic, the grass and wheat field days were cancelled and the potato field day was shortened and switched to a 2 hour online venue that limited the number of speakers by more than two-thirds, so that there was no opportunity to give a talk on bee research advances.  We anticipate reaching these audiences through other products that we are currently developing (e.g., a fact sheet and extension publication).

Journal Publications

The project team is currently revising a peer-reviewed journal article submitted to Molecular Ecology, with the hopes of resubmitting it by October 2020, and plan to submit a second paper to the journal Plos One by December 2020.


Extension Publications, Fact Sheets, and Other Educational Materials

The team is working on producing an extension publication through the Oregon State University Extension Publications that will provide a list of plant species that serve as important food sources for native bees in each of our region, specific guidance for enhancing native bee habitat in field margins on croplands, and examples of ways that producers can profit from enhancing native bee habitat.

In addition, the project team is working on a fact sheet to be distributed both in person and electronically. The factsheet provides a list of plant species (with corresponding photos) that are important food sources for native bees in the area as well as steps for farmers to take toward enhancing native bee habitat on their croplands.

Katie has also volunteered to give a podcast for the PolliNation Podcast series sponsored by the Oregon Bee Project and anticipates recording that in October.

Project Outcomes

Did this project contribute to a larger project?:
Project outcomes:

We have documented interactions between 24 native bee species and 14 flowering plant species in eight field margins of Threemile Canyon Farms so far. These 14 plant species do not include flowering crop species, but we plan to identify the number of crop species detected in the pollen loads in the near future. We will be able to provide Threemile Canyon Farms, and other farms in the area, with a list of significant food sources for the native bees in the area. Farmers can then plant these forage resources in their field margins, which will encourage more crop pollination, increasing the quantity and quality of crop yield. This is especially important in light of economic losses due to colony collapse disorder. Producers may even be able to decrease the amount spent on honey bee hive rentals for pollination services.


Planting forage resources in field margins would benefit the surrounding environment by increasing plant and pollinator diversity. This increase in diversity could result in an increase in natural predators, reducing pest populations and improving the quality of crops at no additional cost. We will also encourage farmers using these sustainable practices to partner with programs like the Xerces Society’s “Bee Better Certified” program. This certification allows them to charge more for their crops, which were grown in ways that support bees and other pollinators. It also provides social benefits, gaining trust with consumers who can find comfort in the fact that their purchase is going to a good cause. 

Knowledge Gained:

During the course of this project, I conducted vegetation surveys and sampled native bees from the field margins at Threemile Canyon Farms. This was my first experience working on a farm that practices sustainable agriculture. I had never seen field margins that were left uncultivated, and I was surprised to see the diversity within them. I was expecting to mostly encounter the European honey bee, Apis mellifera, but there were many native bee species foraging in the field margins. There was also a surprisingly diverse group of flowering plants left growing in the field margins, which serve as forage resources for honey bees and native bees when crops are not blooming.


We collected 24 different native bee species from eight field margins over the course of the study. These bees were observed visiting nine plant species, but we detected an additional five plant species in their pollen loads using DNA metabarcoding techniques. By leaving their field margins uncultivated, Threemile Canyon Farms is able to sustain a diverse group of native pollinators that likely contribute significantly to crop pollination. This is especially important during the continued loss of honey bee colonies to colony collapse disorder. 


Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.