As pollinator populations decline globally, public interest in creating pollinator gardens to help sustain bee abundance and diversity is rising, and there are many lists of recommended pollinator plantings and suggestions for bee-friendly flowers. However, these suggestions often lack grounding in empirical data. While anecdotal and expert suggestions are not without merit, it is crucial that we have clear, replicable, evidence-based planting recommendations to help boost the abundance and diversity of pollinators that will visit the plants. To fill this gap, we created a pollinator garden containing 25 pollinator plantings, some on previous recommendation lists, some not, and that were either native or non-native and perennial or annual. We surveyed the number and taxonomy of pollinator visitors to these plants through non-destructive methods. These data were analyzed to determine which plants are most effective at attracting abundant and diverse flower-visiting insects. We analyzed preference between native and non-native plants, perennials and annuals, and among different plant species. Our data revealed significant variation in the total abundance and diversity of flower-visiting attracted insects among plant species. Brown-eyed Susans attracted the highest abundance of insect visitors (average number of visitors per day = 53) and were 26-fold more attractive than Begonias, which attracted the lowest abundance of insect visitors (average number of visitors per day = 2). Lavender attracted the highest diversity of insect visitors (Simpson's Reciprocal Diversity Score: 3.5) compared to Pineapple Sage (Simpson's Reciprocal Diversity Score: 1.6), which attracted the lowest diversity of insect visitors. Additionally, we found that native perennials significantly attracted the greatest abundance of visitors compared to either non-native annuals or non-native perennials (p < 0.001). We conclude that ornamental landscape plants can support an abundance and diversity of pollinator visitors, and planting schemes should take into consideration the effects of plant species, plant lifespan, and plant origin. We can use these data to better inform the regional community how to attract and support abundant and diverse pollinator populations within urban and sub-urban ornamental landscape gardens. / Master of Science in Life Sciences / Many people want to build gardens with flowers that are attractive to pollinators, which can be bees, flies, butterflies, moths, and other insects. How, though, do we know what should be planted in that garden? Anyone with access to a computer can find lists of recommended plants for a pollinator garden, and they are provided by a variety of sources. However, the plants included on these lists are often not the same. Some lists may claim a handful of plants are the most attractive to pollinators, while another list will suggest different plants as the most attractive. In addition, these lists often are not based on scientific evidence. Our goal was to use these recommendations to build a pollinator garden so that we can collect data on the plants and determine which plants are best in our garden at attracting insect pollinators. We created a pollinator garden that includes 25 of popular landscape plants, many of which are found on lists of current plant recommendation lists. Then we let the plants grow and bloom, and during the summer, we counted and identified the different species of insects found on each plant. We then ranked plants, from best to worst, at their ability to attract insects and pollinators in numbers and in diversity. Additionally, we found that insects are generally more likely to visit plants that are native to the region. Using this information, we can share our results with the public so that anyone can build their own pollinator garden that will be the most attractive to pollinators.

Doctor of Philosophy / Department of Horticulture, Forestry, and Recreation Resources / Dale Bremer / Greg Davis / In 2005, turfgrass was estimated to cover approximately 20 million ha of urbanized land. That area is increasing with rapid urbanization, stressing the importance of water conservation in the lawn and landscape industry. Turfgrasses have been identified for replacement by presumably more water-efficient ornamental plant species to conserve water. However, research comparing drought resistance and evapotranspiration (ET) of turfgrasses with ornamental landscape plants is limited. Two studies were conducted to evaluate water use and performance under drought stress of several ornamental and turfgrass species. An online course was developed to educate students about critical water issues related to irrigation in urbanizing watersheds. In a field study, ET was measured using lysimeters and plant water status was evaluated under deficit irrigation (100%, 60%, and 20% ET) in Festuca arundinacea Schreb., Buchloe dactyloides (Nutt.) Engelm. ‘Sharps Improved’, and Ajuga reptans L. ‘Bronze Beauty’. Evapotranspiration was similar between A. reptans and F. arundinacea, and was 32 and 35% greater than ET of B. dactyloides. In a greenhouse study, the performance of one turfgrass (Poa pratensis L. ‘Apollo’) and eight landscape species (Achillea millifolium L., Ajuga reptans L. ‘Bronze Beauty’, Liriope muscari Decne., Pachysandra terminalis Siebold and Zucc., Sedum album L., Thymus serpyllum L., Vinca major L., and Vinca minor L.) was evaluated during a severe dry down and subsequent recovery. S. album, L. muscari, and P. terminalis performed the best, requiring 86 to 254 d to decline to a quality rating of one (1-9 scale: 1=dead/dormant, 9=best quality). The remaining species required 52 to 63 d. The only species to recover were P. pratensis [46% pot cover (PC) after 60 days], S. album (38% PC), and V. major (35% PC). A survey was developed to measure student learning as it relates to the level of sense and meaning present in the content of a new online course entitled “Water Issues in the Lawn and Landscape.” Survey results were compared with student learning as measured through a post-test. Post-test scores declined as the difference between sense and meaning increased (r =-0.82; P=0.03), indicating student learning is higher when both sense and meaning are present.