Location: Pollinating Insect-Biology, Management, Systematics Research
Project Number: 2080-21000-019-000-D
Project Type: In-House Appropriated
Start Date: Mar 5, 2020
End Date: Mar 4, 2025
Objective:
Objective 1: Improve crop pollination by alfalfa leafcutting bees, bumble bees and mason bees by identifying the environmental and biological factors that impact bee health during propagation and pollination and develop new and improved bee management strategies to ensure healthy, sustainable pollinator populations.
Subobjective 1.1: Improve best management practices for pollinator use in cropping systems that result in sustainable pollinator supply for continued crop pollination.
Subobjective 1.2: Identify impacts of xenobiotic factors on managed bee health (climatic factors, phenological mismatch, temperature range, etc.), host-plant [nutritional value/ host plant chemicals], invasives, pesticides.
Subobjective 1.3: Examine the linkage between nutrition and bee performance in non-Apis bees (immunity, longevity, and reproduction).
Subobjective 1.4: Develop effective treatments of pathogen, pest, and parasites in non-Apis bees.
Subobjective 1.5. Devise new sampling and diagnostic methods for bee pests and diseases.
Objective 2: Improve bee systematics and develop new tools for rapid bee identification to enhance the understanding of wild bee diversity and the identification of environmental and biological factors that promote wild bee sustainability.
Subobjective 2.1: Evaluate bee biodiversity and improve the taxonomic and systematic knowledge needed to achieve effective bee conservation stewardship.
Approach:
Objective 1: Improve crop pollination by alfalfa leafcutting bees, bumble bees and mason bees by identifying the environmental and biological factors that impact bee health during propagation and pollination and develop new and improved bee management strategies to ensure healthy, sustainable pollinator populations.
1.1. Hypotheses will be tested using field studies with measurement of bee health and pollination performance to improve management of mason bees and bumble bees. Experiments will examine interactions of mason with honey bees in co-deployment and impacts on pathogens as detected using molecular methods.
1.2. Exposure to agrichemicals via soil and leaf pieces by solitary bees will be quantified. The hypothesis that sublethal exposure agrichemicals including adjuvants impacts bee health will be tested for honey bees and alfalfa leafcutting bees using experimental manipulation and examine interactions with pathogens.
1.3. Hypotheses will be tested that nutrition (amino acid and sugar sources) can impact the reproduction and life span of alfalfa leafcutting bees. We will determine how the nutritional requirements of a bumble bee colony changes during colony age, as well as the maximal and minimal foraging range of Bombus huntii.
1.4. Hypotheses to examine control of chalkbrood and pollen ball formation via antimicrobial disinfectants will be tested for solitary bees. The life cycle and control of a major emerging parasitoid (Melittobia sp.) in alfalfa leafcutting bees will be determined.
1.5. Molecular methods will identify parasites, parasitoids, and pathogens of mason bees and alkali bees. Non-lethal methods to sample bumble bees parasites and pathogens will be developed. With molecular data, we will identify the species of Melittobia found in managed bees and characterize genetic diversity across populations.
Objective 2: Improve bee systematics and develop new tools for rapid bee identification to enhance the understanding of wild bee diversity and the identification of environmental and biological factors that promote wild bee sustainability.
We will 1) develop up-to-date taxonomies informed by phylogeny, 2) produce web-accessible bee identification tools, and 3) capture biological data present in museum specimens. To accomplish this, we will continue our efforts to survey bees across the western U.S, digitize bee collections, and conduct systematic studies of groups in need of revision. We will use molecular data, especially phylogenomic information derived from DNA sequences using ultra-conserved elements, to build phylogenies and refine species boundaries. The sequence information will be combined with taxonomic keys and images to allow non-experts to more easily identify bees.
